Designing futures for Rust

I recently wrote about the importance of asynchronous I/O in Rust and the aims of the new futures library. This post deepens the story by explaining the core design of that library. If you’re looking for more on the use of the library, you’ll have to wait; we’re very actively working on the Tokio stack and will have more to say once that’s settled down a bit.

To recap, the aim is robust and ergonomic async I/O with no performance penalty:

Robust : the library should have a strong story for error handling, cancellation, timeouts, backpressure, and other typical concerns for writing robust servers. This being Rust, we’ll also of course guarantee thread safety.

Ergonomic : the library should make writing asynchronous code as painless as possible—ideally, as easy as writing synchronous code, but with greater expressivity. While the latter will require async / await to fully achieve, the futures library provides a high-level way of expressing and combining asynchronous computation, similar to Rust’s successful Iterator API.

Zero cost: code written using the library should compile down to something equivalent (or better than) “hand-rolled” server implementations, which would typically use manual state machines and careful memory management.

Achieving these goals requires a mix of existing techniques in Rust, and some new ideas about how to build a futures library; this post will cover both. In a nutshell:

Leverage Rust’s traits and closures for ergonomics and cost-avoidance . Traits and closures in Rust do not require heap allocation or dynamic dispatch—facts we take heavy advantage of. We also use the trait system to package up the futures API in a simple and convenient way.

Design the core Future abstraction to be demand-driven, rather than callback-oriented . (In async I/O terms, follow the “readiness” style rather than the “completion” style.) That means that composing futures together does not involve creating intermediate callbacks. As we’ll see, the approach also has benefits for backpressure and cancellation.

Provide a task abstraction, similar to a green thread, that drives a future to completion. Housing futures within a task is what enables the library code to compile down to the traditional model, i.e., with big state machines that can serve as a callback for a large number of underlying events.

Let’s dive in!

Background: traits in Rust

We’ll start with a quick review of traits in Rust. If you want more reading on these topics, you might check out the longer overview of traits.

To understand how the futures design works, you need to have a basic grasp on Rust’s traits. I won’t attempt a complete introduction here, but I’ll try to hit the most relevant highlights for making sense of what’s going on.

Traits provide Rust’s sole notion of interface, meaning that a trait is an abstraction that can apply to many concrete types. For example, here’s a simplified trait for hashing:

trait Hash { fn hash ( & self ) -> u64 ; }

This trait stipulates that the type implementing it must provide a hash method, which borrows self and produces a u64 . To implement the trait, you have to give a concrete definition for the method, like the following simple-minded one:

impl Hash for bool { fn hash ( & self ) -> u64 { if * self { 0 } else { 1 } } } impl Hash for i32 { ... } // etc

Once these implementations are in place, you can make calls like true.hash() to invoke the method directly. But often the methods are called via generics, which is where traits truly act as an abstraction:

fn print_hash < T : Hash > ( t : & T ) { println ! ( "The hash is {}" , t . hash ()) }

The print_hash function is generic over an unknown type T , but requires that T implements the Hash trait. That means we can use it with bool and i32 values:

print_hash ( & true ); // instantiates T = bool print_hash ( & 12 ); // instantiates T = i32

Generics are compiled away, resulting in static dispatch. That is, as with C++ templates, the compiler will generate two copies of the print_hash method to handle the above code, one for each concrete argument type. That in turn means that the internal call to t.hash() —the point where the abstraction is actually used—has zero cost: it will be compiled to a direct, static call to the relevant implementation:

// The compiled code: __print_hash_bool ( & true ); // invoke specialized bool version directly __print_hash_i32 ( & 12 ); // invoke specialized i32 version directly

Compiling down to non-generic code is essential for making an abstraction like futures work without overhead: most of the time, that non-generic code will also be inlined, letting the compiler produce and optimize large blocks of code that resemble what you might have written in a low-level, “hand-rolled” style.

Closures in Rust work the same way—in fact, they’re just traits. That means, in particular, that creating a closure does not entail heap allocation, and calling a closure can be statically-dispatched, just like the hash method above.

Finally, traits can also be used as “objects”, which cause the trait methods to be dynamically dispatched (so the compiler doesn’t immediately know what implementation a call will use). The benefit to trait objects is for heterogeneous collections, where you need to group together a number of objects which may have different underlying types but all implement the same trait. Trait objects must always be behind a pointer, which in practice usually requires heap allocation.

Defining futures

Now, let’s turn to futures. The earlier post gave an informal definition of a future:

In essence, a future represents a value that might not be ready yet. Usually, the future becomes complete (the value is ready) due to an event happening somewhere else.

Clearly, we’ll want futures to be some kind of trait, since there will be many different kinds of “values that aren’t ready yet” (e.g. data on a socket, the return value from an RPC call, etc.). But how do we represent the “not ready yet” part?

False start: the callback (aka completion-based) approach

There’s a very standard way to describe futures, which we found in every existing futures implementation we inspected: as a function that subscribes a callback for notification that the future is complete.

Note: In the async I/O world, this kind of interface is sometimes referred to as completion-based, because events are signaled on completion of operations; Windows’s IOCP is based on this model.

In Rust terms, the callback model leads to a trait like the following:

trait Future { // The type of value produced by the future type Item ; // Tell the future to invoke the given callback on completion fn schedule < F > ( self , f : F ) where F : FnOnce ( Self :: Item ); }

The FnOnce here is a trait for closures that will be invoked at most once. Because schedule is using generics, it will statically dispatch any calls to that closure.

Unfortunately, this approach nevertheless forces allocation at almost every point of future composition, and often imposes dynamic dispatch, despite our best efforts to avoid such overhead.

To see why, let’s consider a basic way of combining two futures:

fn join < F , G > ( f : F , g : G ) -> impl Future < Item = ( F :: Item , G :: Item ) > where F : Future , G : Future

This function takes two futures, f and g , and returns a new future that yields a pair with results from both. The join ed future completes only when both of the underlying futures complete, but allows the underlying futures to execute concurrently until then.

How would we implement join using the above definition of Future ? The join ed future will be given a single callback both_done which expects a pair. But the underlying futures each want their own callbacks f_done and g_done , taking just their own results. Clearly, we need some kind of sharing here: we need to construct f_done and g_done so that either can invoke both_done , and make sure to include appropriate synchronization as well. Given the type signatures involved, there’s simply no way to do this without allocating (in Rust, we’d use an Arc here).

This kind of problem was repeated in many of the future combinators.

Another problem is that event sources like event loops need to invoke callbacks of arbitrary, different types—a case of the heterogeneity mentioned above. As a concrete example, when a socket is ready for reading, that event will need to be dispatched to some callback, and in general you’ll need a mix of different futures to be in play with different sockets. To make this work, you end up needing to heap-allocate callbacks for the event loop at every point the future wants to listen for an event, and dynamically dispatch notifications to those callbacks.

TL;DR, we were unable to make the “standard” future abstraction provide zero-cost composition of futures, and we know of no “standard” implementation that does so.

What worked: the demand-driven (aka readiness-based) approach

After much soul-searching, we arrived at a new “demand-driven” definition of futures. Here’s a simplified version that ignores the error handling of the real trait:

// A *simplified* version of the trait, without error-handling trait Future { // The type of value produced on success type Item ; // Polls the future, resolving to a value if possible fn poll ( & mut self ) -> Async < Self :: Item > ; } enum Async < T > { /// Represents that a value is immediately ready. Ready ( T ), /// Represents that a value is not ready yet, but may be so later. NotReady , }

The API shift here is straightforward: rather than the future proactively invoking a callback on completion, an external party must poll the future to drive it to completion. The future can signal that it’s not yet ready and must be polled again at some later point by returning Async::NotReady (an abstraction of EWOULDBLOCK ).

Note: In the async I/O world, this kind of interface is sometimes referred to as readiness-based, because events are signaled based on “readiness” of operations (e.g. bytes on a socket being ready) followed by an attempt to complete an operation; Linux’s epoll is based on this model. (This model can also express completion, by treating the completion of an operation as the signal that the future is ready for polling.)

By eliminating all the intermediate callbacks, we’ve addressed some of the key problems of the previous version of the trait. But we’ve introduced a new one: after NotReady is returned, who polls the future, and when do they do so?

Let’s take a concrete example. If a future is attempting to read bytes from a socket, that socket may not be ready for reading, in which case the future can return NotReady . Somehow, we must arrange for the future to later be “woken up” (by calling poll ) once the socket becomes ready. That kind of wakeup is the job of the event loop. But now we need some way to connect the signal at the event loop back to continuing to poll the future.

The solution forms the other main component of the design: tasks.

The cornerstone: tasks

A task is a future that is being executed. That future is almost always made up of a chain of other futures, as in the example from the original post:

id_rpc ( & my_server ). and_then ( | id | { get_row ( id ) }). map ( | row | { json :: encode ( row ) }). and_then ( | encoded | { write_string ( my_socket , encoded ) })

The key point is that there’s a difference between functions like and_then , map and join , which combine futures into bigger futures, and functions that execute futures, like:

The wait method, which simply runs the future as a task pinned to the current thread, blocking that thread until a result is produced and returned.

The spawn method on a thread pool, which launches a future as an independent task on the pool.

These execution functions create a task that contains the future and is responsible for polling it. In the case of wait , polling takes place immediately; for spawn , polling happens once the task is scheduled onto a worker thread.

However polling begins, if any of the interior futures produced a NotReady result, it can grind the whole task to a halt—the task may need to wait for some event to occur before it can continue. In synchronous I/O, this is where a thread would block. Tasks provide an equivalent to this model: the task “blocks” by yielding back to its executor, after installing itself as a callback for the events it’s waiting on.

Returning to the example of reading from a socket, on a NotReady result the task can be added to the event loop’s dispatch table, so that it will be woken up when the socket becomes ready, at which point it will re- poll its future. Crucially, though, the task instance stays fixed for the lifetime of the future it is executing—so no allocation is needed to create or install this callback.

Completing the analogy with threads, tasks provide a park / unpark API for “blocking” and wakeup:

/// Returns a handle to the current task to call unpark at a later date. fn park () -> Task ; impl Task { /// Indicate that the task should attempt to poll its future in a timely fashion. fn unpark ( & self ); }

Blocking a future is a matter of using park to get a handle to its task, putting the resulting Task in some wakeup queue for the event of interest, and returning NotReady . When the event of interest occurs, the Task handle can be used to wake back up the task, e.g. by rescheduling it for execution on a thread pool. The precise mechanics of park / unpark vary by task executor.

In a way, the task model is an instance of “green” (aka lightweight) threading: we schedule a potentially large number of asynchronous tasks onto a much smaller number of real OS threads, and most of those tasks are blocked on some event most of the time. There’s an essential difference from Rust’s old green threading model, however: tasks do not require their own stack. In fact, all of the data needed by a task is contained within its future. That means we can neatly sidestep problems of dynamic stack growth and stack swapping, giving us truly lightweight tasks without any runtime system implications.

Perhaps surprisingly, the future within a task compiles down to a state machine, so that every time the task wakes up to continue polling, it continues execution from the current state—working just like hand-rolled code based on mio. This point is most easily seen by example, so let’s revisit join .

Example: join in the demand-driven model

To implement the join function, we’ll introduce a new concrete type, Join , that tracks the necessary state:

fn join < F : Future , G : Future > ( f : F , g : G ) -> Join < F , G > { Join :: BothRunning ( f , g ) } enum Join < F : Future , G : Future > { BothRunning ( F , G ), FirstDone ( F :: Item , G ), SecondDone ( F , G :: Item ), Done , } impl < F , G > Future for Join < F , G > where F : Future , G : Future { type Item = ( F :: Item , G :: Item ); fn poll ( & mut self ) -> Async < Self :: Item > { // navigate the state machine } }

The first thing to notice is that Join is an enum, whose variants represent states in the “join state machine”:

BothRunning : the two underlying futures are both still executing.

: the two underlying futures are both still executing. FirstDone : the first future has yielded a value, but the second is still executing.

: the first future has yielded a value, but the second is still executing. SecondDone : the second future has yielded a value, but the first is still executing.

: the second future has yielded a value, but the first is still executing. Done : both futures completed, and their values have been returned.

Enums in Rust are represented without requiring any pointers or heap allocation; instead, the size of the enum is the size of the largest variant. That’s exactly what we want—that size represents the “high water mark” of this little state machine.

The poll method here will attempt to make progress through the state machine by poll ing the underlying futures as appropriate.

Recall that the aim of join is to allow its two futures to proceed concurrently, racing to finish. For example, the two futures might each represent subtasks running in parallel on a thread pool. When those subtasks are still running, poll ing their futures will return NotReady , effectively “blocking” the Join future, while stashing a handle to the ambient Task for waking it back up when they finish. The two subtasks can then race to wake up the Task , but that’s fine: the unpark method for waking a task is threadsafe, and guarantees that the task will poll its future at least once after any unpark call. Thus, synchronization is handled once and for all at the task level, without requiring combinators like join to allocate or handle synchronization themselves.

You may have noticed that poll takes &mut self , which means that a given future cannot be poll ed concurrently—the future has unique access to its contents while polling. The unpark synchronization guarantees it.

One final point. Combinators like join embody “small” state machines, but because some of those states involve additional futures, they allow additional state machines to be nested. In other words, poll ing one of the underlying futures for join may involve stepping through its state machine, before taking steps in the Join state machine. The fact that the use of the Future trait does not entail heap allocation or dynamic dispatch is key to making this work efficiently.

In general, the “big” future being run by a task—made up of a large chain of futures connected by combinators—embodies a “big” nested state machine in just this way. Once more, Rust’s enum representation means that the space required is the size of the state in the “big” machine with the largest footprint. The space for this “big” future is allocated in one shot by the task, either on the stack (for the wait executor) or on the heap (for spawn ). After all, the data has to live somewhere—but the key is to avoid constant allocations as the state machine progresses, by instead making space for the entire thing up front.

Futures at scale

We’ve seen the basics of demand-driven futures, but there are a number of concerns about robustness that we also want to cover. It turns out that these concerns are addressed naturally by the demand-driven model. Let’s take a look at a few of the most important.

Cancellation

Futures are often used to represent substantial work that is running concurrently. Sometimes it will become clear that this work is no longer needed, perhaps because a timeout occurred, or the client closed a connection, or the needed answer was found in some other way.

In situations like these, you want some form of cancellation: the ability to tell a future to stop executing because you’re no longer interested in its result.

In the demand-driven model, cancellation largely “falls out”. All you have to do is stop polling the future, instead “dropping” it (Rust’s term for destroying the data). And doing so is usually a natural consequence of nested state machines like Join . Futures whose computation requires some special effort to cancel (such as canceling an RPC call) can provide this logic as part of their Drop implementation.

Backpressure

Another essential aspect of at-scale use of futures (and their close relative, streams) is backpressure: the ability of an overloaded component in one part of a system to slow down input from other components. For example, if a server has a backlog of database transactions for servicing outstanding requests, it should slow down taking new requests.

Like cancellation, backpressure largely falls out of our model for futures and streams. That’s because tasks can be indefinitely “blocked” by a future/stream returning NotReady , and notified to continue polling at a later time. For the example of database transactions, if enqueuing a transaction is itself represented as a future, the database service can return NotReady to slow down requests. Often, such NotReady results cascade backward through a system, e.g. allowing backpressure to flow from the database service back to a particular client connection then back to the overall connection manager. Such cascades are a natural consequence of the demand-driven model.

Communicating the cause of a wakeup

If you’re familiar with interfaces like epoll, you may have noticed something missing from the park / unpark model: it provides no way for a task to know why it was woken up.

That can be a problem for certain kinds futures that involve polling a large number of other futures concurrently—you don’t want to have to re-poll everything to discover which sub-future is actually able to make progress.

To deal with this problem, the library offers a kind of “epoll for everyone”: the ability to associate “unpark events” with a given Task handle. That is, there may be various handles to the same task floating around, all of which can be used to wake the task up, but each of which carries different unpark events. When woken, the future within the task can inspect these unpark events to determine what happened. See the docs for more detail.

Wrapping up

We’ve now seen the core design principles behind the Rust futures and streams library. To recap, it boils down to a few key ideas:

Encapsulate running futures into tasks, which serve as a single, permanent “callback” for the future.

Implement futures in a demand-driven, rather than callback-oriented, style.

Use Rust’s trait system to allow composed futures to flatten into big state machines.

Together, these ideas yield a robust, ergonomic, zero cost futures library.

As I mentioned at the outset of the post, we’re very actively working on the layers above the basic futures library—layers that incorporate particular I/O models (like mio) and also provide yet-higher-level tools for building servers. These layers are part of the Tokio project, and you can read more about the overall vision in my earlier post. As those APIs stabilize, expect to see more posts describing them!