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< h1 id = "底层探秘-future-执行器与任务调度" > < a class = "header" href = "#底层探秘-future-执行器与任务调度" > 底层探秘: Future 执行器与任务调度< / a > < / h1 >
< p > 异步编程背后到底藏有什么秘密？究竟是哪只幕后之手在操纵这一切？如果你对这些感兴趣，就继续看下去，否则可以直接跳过，因为本章节的内容对于一个 API 工程师并没有太多帮助。< / p >
< p > 但是如果你希望能深入理解 < code > Rust< / code > 的 < code > async/.await< / code > 代码是如何工作、理解运行时和性能，甚至未来想要构建自己的 < code > async< / code > 运行时或相关工具，那么本章节终究不会辜负于你。< / p >
< h2 id = "future-特征" > < a class = "header" href = "#future-特征" > Future 特征< / a > < / h2 >
< p > < code > Future< / code > 特征是 Rust 异步编程的核心，毕竟异步函数是异步编程的核心，而 < code > Future< / code > 恰恰是异步函数的返回值和被执行的关键。< / p >
< p > 首先，来给出 < code > Future< / code > 的定义：它是一个能产出值的异步计算(虽然该值可能为空，例如 < code > ()< / code > )。光看这个定义，可能会觉得很空洞，我们来看看一个简化版的 < code > Future< / code > 特征:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > trait SimpleFuture {
type Output;
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> ;
}
enum Poll< T> {
Ready(T),
Pending,
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 在上一章中，我们提到过 < code > Future< / code > 需要被执行器< code > poll< / code > (轮询)后才能运行，诺，这里 < code > poll< / code > 就来了，通过调用该方法，可以推进 < code > Future< / code > 的进一步执行，直到被切走为止( 这里不好理解，但是你只需要知道 < code > Future< / code > 并不能保证在一次 < code > poll< / code > 中就被执行完，后面会详解介绍)。< / p >
< p > 若在当前 < code > poll< / code > 中， < code > Future< / code > 可以被完成，则会返回 < code > Poll::Ready(result)< / code > ，反之则返回 < code > Poll::Pending< / code > ， < code > wake< / code > 函数：当未来 < code > Future< / code > 准备好进一步执行时， 该函数会被调用，然后管理该 < code > Future< / code > 的执行器(例如上一章节中的< code > block_on< / code > 函数)会再次调用 < code > poll< / code > 方法，此时 < code > Future< / code > 就可以继续执行了。< / p >
< p > 如果没有 < code > wake< / code > 方法，那执行器无法知道某个 < code > Future< / code > 是否可以继续被执行，除非执行器定期的轮询每一个 < code > Future< / code > ，确认它是否能被执行，但这种作法效率较低。而有了 < code > wake< / code > ， < code > Future< / code > 就可以主动通知执行器，然后执行器就可以精确的执行该 < code > Future< / code > 。 这种“事件通知 -> 执行”的方式要远比定期对所有 < code > Future< / code > 进行一次全遍历来的高效。< / p >
< p > 也许大家还是迷迷糊糊的，没事，我们用一个例子来说明下。考虑一个需要从 < code > socket< / code > 读取数据的场景：如果有数据，可以直接读取数据并返回 < code > Poll::Ready(data)< / code > ， < code > Future< / code > 会被阻塞且不会再继续执行，此时它会注册一个 < code > wake< / code > 函数，当 < code > socket< / code > 数据准备好时，该函数将被调用以通知执行器：我们的 < code > Future< / code > 已经准备好了，可以继续执行。< / p >
< p > 下面的 < code > SocketRead< / code > 结构体就是一个 < code > Future< / code > :< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > pub struct SocketRead< 'a> {
socket: & 'a Socket,
}
impl SimpleFuture for SocketRead< '_> {
type Output = Vec< u8> ;
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> {
if self.socket.has_data_to_read() {
// socket有数据，
Poll::Ready(self.socket.read_buf())
} else {
// socket中还没数据
//
// 注册一个`wake`函数，当数据可用时，该函数会被调用，
// 然后当前Future的执行器会再次调用`poll`方法，此时就可以读取到数据
self.socket.set_readable_callback(wake);
Poll::Pending
}
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 这种 < code > Future< / code > 模型允许将多个异步操作组合在一起，同时还无需任何内存分配。不仅仅如此，如果你需要同时运行多个 < code > Future< / code > 或链式调用多个 < code > Future< / code > ，也可以通过无内存分配的状态机实现，例如：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > trait SimpleFuture {
type Output;
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> ;
}
enum Poll< T> {
Ready(T),
Pending,
}
/// 一个SimpleFuture，
///
/// 之所以可以并发， ， ， ，
pub struct Join< FutureA, FutureB> {
// 结构体的每个字段都包含一个Future，
// 等到Future完成后， ，
a: Option< FutureA> ,
b: Option< FutureB> ,
}
impl< FutureA, FutureB> SimpleFuture for Join< FutureA, FutureB>
where
FutureA: SimpleFuture< Output = ()> ,
FutureB: SimpleFuture< Output = ()> ,
{
type Output = ();
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> {
// 尝试去完成一个 Future `a`
if let Some(a) = & mut self.a {
if let Poll::Ready(()) = a.poll(wake) {
self.a.take();
}
}
// 尝试去完成一个 Future `b`
if let Some(b) = & mut self.b {
if let Poll::Ready(()) = b.poll(wake) {
self.b.take();
}
}
if self.a.is_none() & & self.b.is_none() {
// 两个 Future都已完成 - 我们可以成功地返回了
Poll::Ready(())
} else {
// 至少还有一个 Future 没有完成任务，因此返回 `Poll::Pending`.
// 当该 Future 再次准备好时，通过调用`wake()`函数来继续执行
Poll::Pending
}
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 上面代码展示了如何同时运行多个 < code > Future< / code > ， < code > Future< / code > 也可以一个接一个的连续运行：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > /// 一个SimpleFuture, 它使用顺序的方式，
//
// 注意: 由于本例子用于演示，因此功能简单，`AndThenFut` 会假设两个 Future 在创建时就可用了.
// 而真实的`Andthen`允许根据第一个`Future`的输出来创建第二个`Future`，因此复杂的多。
pub struct AndThenFut< FutureA, FutureB> {
first: Option< FutureA> ,
second: FutureB,
}
impl< FutureA, FutureB> SimpleFuture for AndThenFut< FutureA, FutureB>
where
FutureA: SimpleFuture< Output = ()> ,
FutureB: SimpleFuture< Output = ()> ,
{
type Output = ();
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> {
if let Some(first) = & mut self.first {
match first.poll(wake) {
// 我们已经完成了第一个 Future，
Poll::Ready(()) => self.first.take(),
// 第一个 Future 还不能完成
Poll::Pending => return Poll::Pending,
};
}
// 运行到这里， ，
self.second.poll(wake)
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 这些例子展示了在不需要内存对象分配以及深层嵌套回调的情况下，该如何使用 < code > Future< / code > 特征去表达异步控制流。 在了解了基础的控制流后，我们再来看看真实的 < code > Future< / code > 特征有何不同之处。< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > trait Future {
type Output;
fn poll(
// 首先值得注意的地方是，`self`的类型从`& mut self`变成了`Pin< & mut Self> `:
self: Pin< & mut Self> ,
// 其次将`wake: fn()` 修改为 `cx: & mut Context< '_> `:
cx: & mut Context< '_> ,
) -> Poll< Self::Output> ;
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 首先这里多了一个 < code > Pin< / code > ，关于它我们会在后面章节详细介绍，现在你只需要知道使用它可以创建一个无法被移动的 < code > Future< / code > ，因为无法被移动，所以它将具有固定的内存地址，意味着我们可以存储它的指针(如果内存地址可能会变动，那存储指针地址将毫无意义！)，也意味着可以实现一个自引用数据结构: < code > struct MyFut { a: i32, ptr_to_a: *const i32 }< / code > 。 而对于 < code > async/await< / code > 来说，< code > Pin< / code > 是不可或缺的关键特性。< / p >
< p > 其次，从 < code > wake: fn()< / code > 变成了 < code > & mut Context< '_> < / code > 。意味着 < code > wake< / code > 函数可以携带数据了，为何要携带数据？考虑一个真实世界的场景，一个复杂应用例如 web 服务器可能有数千连接同时在线，那么同时就有数千 < code > Future< / code > 在被同时管理着，如果不能携带数据，当一个 < code > Future< / code > 调用 < code > wake< / code > 后，执行器该如何知道是哪个 < code > Future< / code > 调用了 < code > wake< / code > ,然后进一步去 < code > poll< / code > 对应的 < code > Future< / code > ？没有办法！那之前的例子为啥就可以使用没有携带数据的 < code > wake< / code > ？ < / p >
< p > 总之，在正式场景要进行 < code > wake< / code > ，就必须携带上数据。 而 < code > Context< / code > 类型通过提供一个 < code > Waker< / code > 类型的值，就可以用来唤醒特定的的任务。< / p >
< h2 id = "使用-waker-来唤醒任务" > < a class = "header" href = "#使用-waker-来唤醒任务" > 使用 Waker 来唤醒任务< / a > < / h2 >
< p > 对于 < code > Future< / code > 来说，第一次被 < code > poll< / code > 时无法完成任务是很正常的。但它需要确保在未来一旦准备好时，可以通知执行器再次对其进行 < code > poll< / code > 进而继续往下执行，该通知就是通过 < code > Waker< / code > 类型完成的。< / p >
< p > < code > Waker< / code > 提供了一个 < code > wake()< / code > 方法可以用于告诉执行器：相关的任务可以被唤醒了，此时执行器就可以对相应的 < code > Future< / code > 再次进行 < code > poll< / code > 操作。< / p >
< h4 id = "构建一个定时器" > < a class = "header" href = "#构建一个定时器" > 构建一个定时器< / a > < / h4 >
< p > 下面一起来实现一个简单的定时器 < code > Future< / code > 。为了让例子尽量简单，当计时器创建时，我们会启动一个线程接着让该线程进入睡眠，等睡眠结束后再通知给 < code > Future< / code > 。< / p >
< p > 注意本例子还会在后面继续使用，因此我们重新创建一个工程来演示：使用 < code > cargo new --lib timer_future< / code > 来创建一个新工程，在 < code > lib< / code > 包的根路径 < code > src/lib.rs< / code > 中添加以下内容：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > use std::{
future::Future,
pin::Pin,
sync::{Arc, Mutex},
task::{Context, Poll, Waker},
thread,
time::Duration,
};
< span class = "boring" > }< / span > < / code > < / pre >
< p > 继续来实现 < code > Future< / code > 定时器，之前提到: 新建线程在睡眠结束后会需要将状态同步给定时器 < code > Future< / code > ，由于是多线程环境，我们需要使用 < code > Arc< Mutex< T> > < / code > 来作为一个共享状态，用于在新线程和 < code > Future< / code > 定时器间共享。< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > pub struct TimerFuture {
shared_state: Arc< Mutex< SharedState> > ,
}
/// 在Future和等待的线程间共享状态
struct SharedState {
/// 定时(睡眠)是否结束
completed: bool,
/// 当睡眠结束后，线程可以用`waker`通知`TimerFuture`来唤醒任务
waker: Option< Waker> ,
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 下面给出 < code > Future< / code > 的具体实现:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > impl Future for TimerFuture {
type Output = ();
fn poll(self: Pin< & mut Self> , cx: & mut Context< '_> ) -> Poll< Self::Output> {
// 通过检查共享状态，来确定定时器是否已经完成
let mut shared_state = self.shared_state.lock().unwrap();
if shared_state.completed {
Poll::Ready(())
} else {
// 设置`waker`，这样新线程在睡眠(计时)结束后可以唤醒当前的任务，接着再次对`Future`进行`poll`操作,
//
// 下面的`clone`每次被`poll`时都会发生一次，实际上，应该是只`clone`一次更加合理。
// 选择每次都`clone`的原因是： `TimerFuture`可以在执行器的不同任务间移动，如果只克隆一次，
// 那么获取到的`waker`可能已经被篡改并指向了其它任务，最终导致执行器运行了错误的任务
shared_state.waker = Some(cx.waker().clone());
Poll::Pending
}
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 代码很简单，只要新线程设置了 < code > shared_state.completed = true< / code > ，那任务就能顺利结束。如果没有设置，会为当前的任务克隆一份 < code > Waker< / code > ，这样新线程就可以使用它来唤醒当前的任务。< / p >
< p > 最后，再来创建一个 API 用于构建定时器和启动计时线程:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > impl TimerFuture {
/// 创建一个新的`TimerFuture`，在指定的时间结束后，该`Future`可以完成
pub fn new(duration: Duration) -> Self {
let shared_state = Arc::new(Mutex::new(SharedState {
completed: false,
waker: None,
}));
// 创建新线程
let thread_shared_state = shared_state.clone();
thread::spawn(move || {
// 睡眠指定时间实现计时功能
thread::sleep(duration);
let mut shared_state = thread_shared_state.lock().unwrap();
// 通知执行器定时器已经完成，可以继续`poll`对应的`Future`了
shared_state.completed = true;
if let Some(waker) = shared_state.waker.take() {
waker.wake()
}
});
TimerFuture { shared_state }
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 至此，一个简单的定时器 < code > Future< / code > 就已创建成功，那么该如何使用它呢？相信部分爱动脑筋的读者已经猜到了：我们需要创建一个执行器，才能让程序动起来。< / p >
< h2 id = "执行器-executor" > < a class = "header" href = "#执行器-executor" > 执行器 Executor< / a > < / h2 >
< p > Rust 的 < code > Future< / code > 是惰性的：只有屁股上拍一拍，它才会努力动一动。其中一个推动它的方式就是在 < code > async< / code > 函数中使用 < code > .await< / code > 来调用另一个 < code > async< / code > 函数，但是这个只能解决 < code > async< / code > 内部的问题，那么这些最外层的 < code > async< / code > 函数，谁来推动它们运行呢？答案就是我们之前多次提到的执行器 < code > executor< / code > 。< / p >
< p > 执行器会管理一批 < code > Future< / code > (最外层的 < code > async< / code > 函数)，然后通过不停地 < code > poll< / code > 推动它们直到完成。 最开始，执行器会先 < code > poll< / code > 一次 < code > Future< / code > ，后面就不会主动去 < code > poll< / code > 了，而是等待 < code > Future< / code > 通过调用 < code > wake< / code > 函数来通知它可以继续，它才会继续去 < code > poll< / code > 。这种 < strong > wake 通知然后 poll< / strong > 的方式会不断重复，直到 < code > Future< / code > 完成。< / p >
< h4 id = "构建执行器" > < a class = "header" href = "#构建执行器" > 构建执行器< / a > < / h4 >
< p > 下面我们将实现一个简单的执行器，它可以同时并发运行多个 < code > Future< / code > 。例子中，需要用到 < code > futures< / code > 包的 < code > ArcWake< / code > 特征，它可以提供一个方便的途径去构建一个 < code > Waker< / code > 。编辑 < code > Cargo.toml< / code > ，添加下面依赖:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > [dependencies]
futures = "0.3"
< span class = "boring" > }< / span > < / code > < / pre >
< p > 在之前的内容中，我们在 < code > src/lib.rs< / code > 中创建了定时器 < code > Future< / code > ，现在在 < code > src/main.rs< / code > 中来创建程序的主体内容，开始之前，先引入所需的包：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > use {
futures::{
future::{BoxFuture, FutureExt},
task::{waker_ref, ArcWake},
},
std::{
future::Future,
sync::mpsc::{sync_channel, Receiver, SyncSender},
sync::{Arc, Mutex},
task::{Context, Poll},
time::Duration,
},
// 引入之前实现的定时器模块
timer_future::TimerFuture,
};
< span class = "boring" > }< / span > < / code > < / pre >
< p > 执行器需要从一个消息通道( < code > channel< / code > )中拉取事件，然后运行它们。当一个任务准备好后（可以继续执行），它会将自己放入消息通道中，然后等待执行器 < code > poll< / code > 。< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > /// 任务执行器，负责从通道中接收任务然后执行
struct Executor {
ready_queue: Receiver< Arc< Task> > ,
}
/// `Spawner`负责创建新的`Future`然后将它发送到任务通道中
#[derive(Clone)]
struct Spawner {
task_sender: SyncSender< Arc< Task> > ,
}
/// 一个Future，
struct Task {
/// 进行中的Future，
///
/// 按理来说`Mutex`在这里是多余的，因为我们只有一个线程来执行任务。但是由于
/// Rust并不聪明，
/// 我们需要使用`Mutex`来满足这个笨笨的编译器对线程安全的执着。
///
/// 如果是生产级的执行器实现，不会使用`Mutex`，因为会带来性能上的开销，取而代之的是使用`UnsafeCell`
future: Mutex< Option< BoxFuture< 'static, ()> > > ,
/// 可以将该任务自身放回到任务通道中，
task_sender: SyncSender< Arc< Task> > ,
}
fn new_executor_and_spawner() -> (Executor, Spawner) {
// 任务通道允许的最大缓冲数(任务队列的最大长度)
// 当前的实现仅仅是为了简单，在实际的执行中，并不会这么使用
const MAX_QUEUED_TASKS: usize = 10_000;
let (task_sender, ready_queue) = sync_channel(MAX_QUEUED_TASKS);
(Executor { ready_queue }, Spawner { task_sender })
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 下面再来添加一个方法用于生成 < code > Future< / code > , 然后将它放入任务通道中:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > impl Spawner {
fn spawn(& self, future: impl Future< Output = ()> + 'static + Send) {
let future = future.boxed();
let task = Arc::new(Task {
future: Mutex::new(Some(future)),
task_sender: self.task_sender.clone(),
});
self.task_sender.send(task).expect("任务队列已满");
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 在执行器 < code > poll< / code > 一个 < code > Future< / code > 之前，首先需要调用 < code > wake< / code > 方法进行唤醒，然后再由 < code > Waker< / code > 负责调度该任务并将其放入任务通道中。创建 < code > Waker< / code > 的最简单的方式就是实现 < code > ArcWake< / code > 特征，先来为我们的任务实现 < code > ArcWake< / code > 特征，这样它们就能被转变成 < code > Waker< / code > 然后被唤醒:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > impl ArcWake for Task {
fn wake_by_ref(arc_self: & Arc< Self> ) {
// 通过发送任务到任务管道的方式来实现`wake`，这样`wake`后，任务就能被执行器`poll`
let cloned = arc_self.clone();
arc_self
.task_sender
.send(cloned)
.expect("任务队列已满");
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 当任务实现了 < code > ArcWake< / code > 特征后，它就变成了 < code > Waker< / code > ，在调用 < code > wake()< / code > 对其唤醒后会将任务复制一份所有权( < code > Arc< / code > )，然后将其发送到任务通道中。最后我们的执行器将从通道中获取任务，然后进行 < code > poll< / code > 执行：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > impl Executor {
fn run(& self) {
while let Ok(task) = self.ready_queue.recv() {
// 获取一个future， ， ，
let mut future_slot = task.future.lock().unwrap();
if let Some(mut future) = future_slot.take() {
// 基于任务自身创建一个 `LocalWaker`
let waker = waker_ref(& task);
let context = & mut Context::from_waker(& *waker);
// `BoxFuture< T> `是`Pin< Box< dyn Future< Output = T> + Send + 'static> > `的类型别名
// 通过调用`as_mut`方法，可以将上面的类型转换成`Pin< & mut dyn Future + Send + 'static> `
if future.as_mut().poll(context).is_pending() {
// Future还没执行完， ，
*future_slot = Some(future);
}
}
}
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 恭喜！我们终于拥有了自己的执行器，下面再来写一段代码使用该执行器去运行之前的定时器 < code > Future< / code > ： < / p >
< pre class = "playground" > < code class = "language-rust edition2021" > fn main() {
let (executor, spawner) = new_executor_and_spawner();
// 生成一个任务
spawner.spawn(async {
println!("howdy!");
// 创建定时器Future，
TimerFuture::new(Duration::new(2, 0)).await;
println!("done!");
});
// drop掉任务， ，
drop(spawner);
// 运行执行器直到任务队列为空
// 任务运行后，会先打印`howdy!`, 暂停2秒，
executor.run();
}< / code > < / pre >
< h2 id = "执行器和系统-io" > < a class = "header" href = "#执行器和系统-io" > 执行器和系统 IO< / a > < / h2 >
< p > 前面我们一起看过一个使用 < code > Future< / code > 从 < code > Socket< / code > 中异步读取数据的例子:< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > pub struct SocketRead< 'a> {
socket: & 'a Socket,
}
impl SimpleFuture for SocketRead< '_> {
type Output = Vec< u8> ;
fn poll(& mut self, wake: fn()) -> Poll< Self::Output> {
if self.socket.has_data_to_read() {
// socket有数据，
Poll::Ready(self.socket.read_buf())
} else {
// socket中还没数据
//
// 注册一个`wake`函数，当数据可用时，该函数会被调用，
// 然后当前Future的执行器会再次调用`poll`方法，此时就可以读取到数据
self.socket.set_readable_callback(wake);
Poll::Pending
}
}
}
< span class = "boring" > }< / span > < / code > < / pre >
< p > 该例子中，< code > Future< / code > 将从 < code > Socket< / code > 读取数据，若当前还没有数据，则会让出当前线程的所有权，允许执行器去执行其它的 < code > Future< / code > 。当数据准备好后，会调用 < code > wake()< / code > 函数将该 < code > Future< / code > 的任务放入任务通道中，等待执行器的 < code > poll< / code > 。< / p >
< p > 关于该流程已经反复讲了很多次，相信大家应该非常清楚了。然而该例子中还有一个疑问没有解决：< / p >
< ul >
< li > < code > set_readable_callback< / code > 方法到底是怎么工作的？怎么才能知道 < code > socket< / code > 中的数据已经可以被读取了？< / li >
< / ul >
< p > 关于第二点，其中一个简单粗暴的方法就是使用一个新线程不停的检查 < code > socket< / code > 中是否有了数据，当有了后，就调用 < code > wake()< / code > 函数。该方法确实可以满足需求，但是性能着实太低了，需要为每个阻塞的 < code > Future< / code > 都创建一个单独的线程！< / p >
< p > 在现实世界中，该问题往往是通过操作系统提供的 < code > IO< / code > 多路复用机制来完成，例如 < code > Linux< / code > 中的 < strong > < code > epoll< / code > < / strong > ， < code > FreeBSD< / code > 和 < code > macOS< / code > 中的 < strong > < code > kqueue< / code > < / strong > ， < code > Windows< / code > 中的 < strong > < code > IOCP< / code > < / strong > , < code > Fuchisa< / code > 中的 < strong > < code > ports< / code > < / strong > 等(可以通过 Rust 的跨平台包 < code > mio< / code > 来使用它们)。借助 IO 多路复用机制，可以实现一个线程同时阻塞地去等待多个异步 IO 事件，一旦某个事件完成就立即退出阻塞并返回数据。相关实现类似于以下代码：< / p >
< pre class = "playground" > < code class = "language-rust edition2021" > < span class = "boring" > #![allow(unused)]
< / span > < span class = "boring" > fn main() {
< / span > struct IoBlocker {
/* ... */
}
struct Event {
// Event的唯一ID， ，
id: usize,
// 一组需要等待或者已发生的信号
signals: Signals,
}
impl IoBlocker {
/// 创建需要阻塞等待的异步IO事件的集合
fn new() -> Self { /* ... */ }
/// 对指定的IO事件表示兴趣
fn add_io_event_interest(
& self,
/// 事件所绑定的socket
io_object: & IoObject,
event: Event,
) { /* ... */ }
/// 进入阻塞，直到某个事件出现
fn block(& self) -> Event { /* ... */ }
}
let mut io_blocker = IoBlocker::new();
io_blocker.add_io_event_interest(
& socket_1,
Event { id: 1, signals: READABLE },
);
io_blocker.add_io_event_interest(
& socket_2,
Event { id: 2, signals: READABLE | WRITABLE },
);
let event = io_blocker.block();
// 当socket的数据可以读取时，
println!("Socket {:?} is now {:?}", event.id, event.signals);
< span class = "boring" > }< / span > < / code > < / pre >
< p > 这样，我们只需要一个执行器线程，它会接收 IO 事件并将其分发到对应的 < code > Waker< / code > 中，接着后者会唤醒相关的任务，最终通过执行器 < code > poll< / code > 后，任务可以顺利地继续执行, 这种 IO 读取流程可以不停的循环，直到 < code > socket< / code > 关闭。< / p >
< / main >
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