关于rust:Rust-You-Dont-Know

Rust 语言作为一种强调性能、平安和并发性的新的编程语言，正日益受到程序员们的关注。Rust 语言曾经间断 7 年蝉联 StackOverflow 网站（寰球最大的编程问答网站）最受欢语言。甚至 Linus Torvalds 认为 Rust 行将成为 Linux 的官网第二语言。有理由置信越来越多的程序员将退出尝试学习 Rust。但 Rust 语言的学习曲线比拟平缓，门槛不低。因而，达坦科技的联结创始人兼 CTO 施继成 将本人学习和使用 Rust 语言的心得体会集结成书，咱们也将在达坦科技（DatenLord）公众号陆续连载。

这些思维的火花将不同于市面上其余对于学习 Rust 编程语言的教科书，它更多地将向程序员分享学习 Rust 语言的根本要义，以及在理论应用场景下如何使用 Rust 解决问题的思考，从而让 Rust 真正变成一种活生生的、有呼吸的、有用的语言。

本文是 Rust You Don’t Know 的第一章。

Chapter One: Process, Thread, and Coroutine

Before we start discussing the asynchronization of Rust, we’d better firstly talk about how the operating system organizes and schedules the tasks, which will help us understand the motivation of the language-level asynchronization mechanisms.

Process and thread

People always want to run multiple tasks simultaneously on the OS even though there’s only one CPU core because one task usually can’t occupy the whole CPU core at most times. Following the idea, we have to answer two questions to get the final design, how to abstract the task and how to schedule the tasks on the hardware CPU core.

Usually, we don’t want tasks to affect each other, which means they can run separately and manage their states. As states are stored in the memory, tasks must hold their own memory space to achieve the above goal. For instance, the execution flow is a kind of in-memory state, recording the current instruction position and the on-stack states. In one word, processes are tasks having separated memory spaces on Linux.

Though memory space separation is one of the key features of processes, they sometimes have to share some memory. First, the kernel code is the same across all processes, kernel part memory space sharing reduces unnecessary memory redundant. Secondly, processes need to cooperate so that inter-process communications (IPC) are unavoidable, and most high-performance IPCs are some kind of memory sharing/transferring. Considering the above requirements sharing the whole memory space across tasks is more convenient in some scenarios, where thread helps.

A process can contain one (single-thread process) or more threads. Threads in a process share the same memory space, which means most state changes are observable by all these threads except for the execution stacks. Each thread has its execution flow and can run on any CPU core concurrently.

Now we know that process and thread are the basic execution units/tasks on most OSes, let’s try to run them on the real hardware, CPU cores.

Schedule

The first challenge we meet when trying to run processes and threads is the limited hardware resources, the CPU core number is limited. When I write this section, one x86 CPU can at most run 128 tasks at the same time, AMD Ryzen™ Threadripper™ PRO 5995WX Processor. But it’s too easy to create thousands of processes or threads on Linux, we have to decide how to place them on the CPU core and when to stop a task, where OS task scheduler helps.

Schedulers can interrupt an executing task regardless of its state, and schedule a new one. It’s called preemptive schedule and is used by most OSes like Linux. The advantage is that it can share the CPU time slice between tasks fairly no matter what they’re running, but the tasks have no idea about the scheduler. To interrupt a running task, hardware interruption like time interruption is necessary.

The other schedulers are called non-preemptive schedulers, which have to cooperate with the task while scheduling. Here tasks are not interrupted, instead, they decide when to release the computing resource. The tasks usually schedule themselves out when doing I/O operations, which usually take a while to complete. Fairness is hard to be guaranteed as the task itself may run forever without stopping, in which case other tasks have no opportunity to be scheduled on that core.

No matter what kind of scheduler is taken, tasks scheduling always needs to do the following steps:

• Store current process/thread execution flow information.
• Change page table mapping (memory space) and flush TLB if necessary.
• Restore the new process/thread execution flow from the previously stored state.

After adopting a scheduler operating system can run tens of thousands of processes/threads on the limited hardware resource.

Coroutine

We have basic knowledge of OS scheduling, and it seems to work fine in most cases. Next, let’s see how it performs in extreme scenarios. Free software developer, Jim Blandy, did an interesting test to show how much time it takes to do a context switch on Linux. In the test, the app creates 500 thread and connect them with pipes like a chain, and then pass a one-byte message from one side to the other side. The whole test runs 10000 iterations to get a stable result. The result shows that a thread context switch takes around 1.7us, compared to 0.2us of a Rust async task switch.

It’s the first time to mention “Rust async task”, which is a concrete implementation of coroutine in Rust. The coroutines are lightweight tasks for non-preemptive multitasking, whose execution can be suspended and resumed. Usually, the task itself decides when to suspend and wait for a notification to resume. To suspend and resume tasks’ execution flow, the execution states should be saved, just like what OS does. Saving the CPU register values is easy for the OS, but not for the applications. Rust saves it to a state machine, and the machine can only be suspended and resumed from the valid states in that machine. To make it easy, We name the state machine “Future”.

Future

We all know that the Future is the data structure returned from an async function, an async block is also a future. When we get it, it does nothing, it’s just a plan and a blueprint, telling us what it’s going to do. Let’s see the example below:

async fn async_fn() -> u32 {return 0;}

We can’t see any “Future” structure in the function definition, but the compiler will translate the function signature to another one returning a “Future”:

fn async_fn() -> Future<Output=u32> {...}

Rust compiler does us a great favor to generate the state machine for us. Here’s the Futures API from std lib:

pub trait Future {
    type Output;

    fn poll(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Self::Output>;
}

pub enum Poll<T> {Ready(T),
    Pending,
}

The poll function tries to drive the state machine until a final result Output is returned. The state machine is a black box for the caller of the poll function, since that Poll::Pending means it’s not in the final state, and Poll::Ready(T) means it’s in the final state. Whenever the Poll::Pending is returned it means the coroutine is suspended. Every call to poll is trying to resume the coroutine.

Runtime

Since Futures are state machines, there should be a driver that pushes the machine state forward. Though we can write the driver manually by polling the Futures one by one until we get the final result, that work should be done once and reused everywhere, in the result the runtime comes. A Rust async runtime handles the following tasks:

1. Drive the received Futures forward.
2. Park or store the blocked Futures.

3. Get notification to restore or resume the blocked Futures.

   Summary

In this chapter, we learned that “Rust async” is a way to schedule tasks. And the execution state is stored in a state machine named Future. In the next chapters, we’ll discuss Future automatical generation by the compiler and its optimizations.