The Detroit Post
Thursday, 21 October, 2021

Rust For Futures

author
Elaine Sutton
• Monday, 09 November, 2020
• 7 min read

This crate provides a number of core abstractions for writing asynchronous code: Futures are single eventual values produced by asynchronous computations.

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(Source: www.thebalance.com)

Contents

Some programming languages (e.g. JavaScript) call this concept “promise”. Streams represent a series of values produced asynchronously.

Sinks provide support for asynchronous writing of data. Large asynchronous computations are built up using futures, streams and sinks, and then spawned as independent tasks that are run to completion, but do not block the thread running them.

The following example describes how the task system context is built and used within macros and keywords such as asynchronous and await!. The majority of examples and code snippets in this crate assume that they are inside an asynchronous block as written above.

Join Polls multiple futures simultaneously, returning a tuple of all results once complete. Try_join Polls multiple futures simultaneously, resolving to a Result containing either a tuple of the successful outputs or an error.

Asynchronous in Rust uses a Poll based approach, in which an asynchronous task will have three phases. It's now up to the executor which polled the Future in step 1 to schedule the future to be polled again and make further progress until it completes or reaches a new point where it can't make further progress and the cycle repeats.

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(Source: www.thebalance.com)

It's unlikely that you'll implement a leaf future yourself unless you're writing a runtime, but we'll go through how they're constructed in this book as well. It's also unlikely that you'll pass a leaf-future to a runtime and run it to completion alone as you'll understand by reading the next paragraph.

The bulk of an asynchronous program will consist of non-leaf- futures, which are a kind of pause-able computation. The key to these tasks is that they're able to yield control to the runtime's scheduler and then resume execution again where it left off at a later point.

When we poll these futures we either run some code or we yield to the scheduler while waiting for some resource to signal us that it's ready, so we can resume where we left off. Languages like C#, JavaScript, Java, GO, and many others comes with a runtime for handling concurrency.

The difference between Rust and other languages is that you have to make an active choice when it comes to picking a runtime. I find it easier to reason about how Futures work by creating a high level mental model we can use.

A fully working asynchronous system in Rust can be divided into three parts: The Water is how the reactor tells the executor that a specific Future is ready to run.

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A common, but not required, method is to create a new Water for each Future that is registered with the executor. This design is what gives the futures framework it's power and flexibility and allows the Rust standard library to provide an ergonomic, zero-cost abstraction for us to use.

In an effort to try to visualize how these parts work together I put together a set of slides in the next chapter that I hope will help. An ergonomic way of creating tasks which can be suspended and resumed through the asynchronous and await keywords.

A defined interface to wake up a suspended task through the Water type. Now, as you'll see when we go through how Futures work, the code we write between the yield points are run on the same thread as our executor.

That means that while our analyzer is working on the dataset, the executor is busy doing calculations instead of handling new requests. The runtime could have some kind of supervisor that monitors how much time different tasks take, and move the executor itself to a different thread so it can continue to run even though our analyzer task is blocking the original executor thread.

You can create a reactor yourself which is compatible with the runtime which does the analysis any way you see fit, and returns a Future which can be awaited. The problem with #2 is that if you switch runtime you need to make sure that it supports this kind of supervision as well or else you will end up blocking the executor.

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(Source: metals.about.com)

#3 is more of theoretical importance, normally you'd be happy by sending the task to the thread-pool most runtimes provide. Now, armed with this knowledge you are already on a good way for understanding Futures, but we're not going to stop yet, there are lots of details to cover.

Learning these concepts by studying futures is making it much harder than it needs to be, so go on and read these chapters if you feel a bit unsure. In this section we take a deeper look at some advantages of having a loose coupling between the Executor-part and Reactor-part of an asynchronous runtime.

One of the reasons for this design is that it allows different Reactors the ability to Wake a Future. One way to achieve this would be to add an AtomicBool to the instance of the future, and an extra method called cancel().

The main reason for designing the Future in this manner is because we don't have to modify either the Executor or the other Reactors; they are all oblivious to the change. Just be aware that if other Futures are awaited ing it, they won't be able to start until Ready is returned.

The Tokyo crate is stable, easy to use, and lightning fast. Futures are already in the standard library** but in this series of blog posts, I'm going to write a simplified version of that library to show how it works, how to use it, and avoid some common pitfalls.

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(Source: www.mississippi-crops.com)

Things are moving quickly, much of what is in that crate will end up in the standard library eventually. The goal of this post is to be able to understand this code, and to implement the types and functions required to make this compile.

The documentation for the futures crate calls it a concept for an object which is a proxy for another value that may not be ready yet.” Futures in rust allow you to define a task, like a network call or computation, to be run asynchronously.

You can chain functions onto that result, transform it, handle errors, merge it with other futures, and perform many other computations on it. In JavaScript, promises are executed on the event loop and there is no other choice to run them.

But, in essence, the promise still simply defines a set of instructions to be run later. In Rust, the executor could use any of a number of asynchronous strategies to run.

The details of how this work are not important to understanding how futures are created and chained together, so our executor is a very rough approximation of one. It can only run one future, and it can't do any meaningful asynchronous.

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(Source: www.cherokeeforum.com)

The Tokyo documentation has a lot more information about the runtime model of futures. The body of the function is an approximation of what a real runner might do, it loops until it gets notified that the future is ready to be polled again.

This trait is simple for now and simply declares the required type, Output, and the signature of the only required method, poll which takes a reference to a context object. This object has a reference to a water, which is used to notify the runtime that the future is ready to be polled again.

# automatically creates a ::default() function for the type. In our implementation of poll we are deciding what to do based on the internal count field.

So let's create a super-handy future to chain it with that adds 1 to any type that can have 1 added to it, for example Future. Impl Future for AddOneFuture is a generic trait implementation.

T: Future ensures that anything that is wrapped by AddOneFuture implements Future T::Item: std::ops::Add ensures that the value represented by Poll::Ready(value) responds to the + operation. There are just a couple easy steps to building those chaining functions(combinations) that give futures a lot of their power.

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(Source: acreativemess.nl)

Futures are a powerful way to leverage Rust's concept of zero cost abstractions to make readable, fast asynchronous code. Futures behave a lot like promises in JavaScript and other languages.

We've learned a lot about constructing generic types and a little about chaining actions together. Combinations, in a non-technical sense, allow you to use functions (like callbacks) to build a new type.

This book aims to explain Futures in Rust using an example driven approach, exploring why they're designed the way they are, and how they work. Going into the level of detail I do in this book is not needed to use futures or asynchronous/await in Rust.

This book will try to explain everything you might wonder about up until the topic of different types of executors and runtimes. We'll just implement a very simple runtime in this book introducing some concepts but it's enough to get started.

Stefan Gavin has made an excellent series of articles about asynchronous runtimes and executors, and if the rumors are right there is more to come from him in the near future. Any suggestions or improvements can be filed as a PR or in the issue tracker for the book.

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(Source: entermyattic.com)

I'd like to take this chance to thank the people behind mid, Tokyo, async_std, futures, LBC, crossbeam which underpins so much of the asynchronous ecosystem and rarely gets enough praise in my eyes. A special thanks to Jonson who was kind enough to give me some valuable feedback on a very early draft of this book.

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