在前面几篇关于数据库引擎的讨论里很多的运算函数都返回了scala.Future类型的结果，因为我以为这样就可以很方便的实现了non-blocking效果。无论任何复杂的数据处理操作，只要把它们包在一个Future{…}里扔给系统运算就算完事不理了，马上可以把关注放到编程的其它部分了。在3月17日的深圳scala用户meetup里我做了个关于scala函数式编程的分享，里面我提到现在使用最多的函数组件就是scala.Future了。我想这应该在scala用户群里是个比较普遍的现象：大家都认为这是实现non-blocking最直接的一种方式。不过当我在meetup后回想到scala.Future时突然意识到它是一种即时运算值strict-value，看看下面这个例子：
import scala.concurrent.duration._
val fs = Future {println("run now..."); System.currentTimeMillis() }
//> run now...
//| fs : scala.concurrent.Future[Long] = List()
Await.result(fs, 1.second) //> res0: Long = 1465907784714
Thread.sleep(1000)
Await.result(fs, 1.second) //> res1: Long = 1465907784714
可以看到fs是在Future构建时即时运算的，而且只会运算一次。如果scala Future中包括了能产生副作用的代码，在构建时就会立即产生副作用。所以我们是无法使用scala Future来编写纯函数的，如下：
val progA：Future[A] = for {
b <- readFromB
_ <- writeToLocationA(a)
r <- getResult
} yield r
/* location A content updated */
... /* later */
val progB: Future[B] = for {
a <- readFromA
_ <- updateLocationA
c <- getResult
}
...
val program: Future[Unit] = for {
_ <- progA
_ <- progB
} yield()
在上面这个例子里最终的目的是运算program：由progA,progB两个子程序组成。这两个子程序在构建的时候已经开始了运算，随时都会更新localionA产生副作用。想象一下如果progA，progB是埋藏在其它一大堆源代码里的话program的运算结果肯定是无法预测的。换言之用Future来进行函数式组合就是在给自己挖坑嘛，最起码要记住这些Future的构建顺序，而这个要求在大型的协作开发软件工程里基本上是不可能的事。除了无法安全进行函数组合外scala.Future还缺少运算和线程控制的功能，比如：
无法控制什么时候开始运算
无法控制在在哪个线程运算
无法终止开始运算的程序
缺少有效的异常处理机制如fallback，retry等
scalaz和monix函数组件库里都提供了Task来辅助Future实现函数组合。scalaz.Task是基于scalaz.Future的：
sealed abstract class Future[+A] {
...
object Future {
case class Now[+A](a: A) extends Future[A]
case class Async[+A](onFinish: (A => Trampoline[Unit]) => Unit) extends Future[A]
case class Suspend[+A](thunk: () => Future[A]) extends Future[A]
case class BindSuspend[A,B](thunk: () => Future[A], f: A => Future[B]) extends Future[B]
case class BindAsync[A,B](onFinish: (A => Trampoline[Unit]) => Unit,
f: A => Future[B]) extends Future[B]
...
scalaz.Future[A]明显就是个Free Monad。它的结构化表达方式分别有Now,Async,Suspend,BindSuspend,BindAsync。我们可以用这些结构实现flatMap函数，所以Future就是Free Monad：
def flatMap[B](f: A => Future[B]): Future[B] = this match {
case Now(a) => Suspend(() => f(a))
case Suspend(thunk) => BindSuspend(thunk, f)
case Async(listen) => BindAsync(listen, f)
case BindSuspend(thunk, g) =>
Suspend(() => BindSuspend(thunk, g andThen (_ flatMap f)))
case BindAsync(listen, g) =>
Suspend(() => BindAsync(listen, g andThen (_ flatMap f)))
}
因为free structure类型支持算式／算法关注分离，我们可以用scalaz.Future来描述程序功能而不涉及正真运算。这样，在上面那个例子里如果progA,progB是Task类型的，那么program的构建就是安全的，因为我们最后是用Task.run来真正进行运算产生副作用的。scalaz.Task又在scalaz.Future功能基础上再增加了异常处理等功能。
monix.Task采取了延迟运算的方式来实现算式/算法分离，下面是这个类型的基础构建结构：
/** [[Task]] state describing an immediate synchronous value. */
private[eval] final case class Now[A](value: A) extends Task[A] {...}
/** [[Task]] state describing an immediate synchronous value. */
private[eval] final case class Eval[A](thunk: () => A)
extends Task[A]
/** Internal state, the result of [[Task.defer]] */
private[eval] final case class Suspend[+A](thunk: () => Task[A])
extends Task[A]
/** Internal [[Task]] state that is the result of applying `flatMap`. */
private[eval] final case class FlatMap[A, B](source: Task[A], f: A => Task[B])
extends Task[B]
/** Internal [[Coeval]] state that is the result of applying `map`. */
private[eval] final case class Map[S, +A](source: Task[S], f: S => A, index: Int)
extends Task[A] with (S => Task[A]) {
def apply(value: S): Task[A] =
new Now(f(value))
override def toString: String =
super[Task].toString
}
/** Constructs a lazy [[Task]] instance whose result will
* be computed asynchronously.
*
* Unsafe to build directly, only use if you know what you're doing.
* For building `Async` instances safely, see [[create]].
*/
private[eval] final case class Async[+A](register: (Context, Callback[A]) => Unit)
extends Task[A]
下面的例子里示范了如果用这些结构来构件monix.Task：
object Task extends TaskInstancesLevel1 { /** Returns a new task that, when executed, will emit the result of * the given function, executed asynchronously. * * This operation is the equivalent of: * {{{ * Task.eval(f).executeAsync * }}} * * @param f is the callback to execute asynchronously */ def apply[A](f: => A): Task[A] = eval(f).executeAsync /** Returns a `Task` that on execution is always successful, emitting * the given strict value. */ def now[A](a: A): Task[A] = Task.Now(a) /** Lifts a value into the task context. Alias for [[now]]. */ def pure[A](a: A): Task[A] = now(a) /** Returns a task that on execution is always finishing in error * emitting the specified exception. */ def raiseError[A](ex: Throwable): Task[A] = Error(ex) /** Promote a non-strict value representing a Task to a Task of the * same type. */ def defer[A](fa: => Task[A]): Task[A] = Suspend(fa _) ...} source match { case Task.Now(v) => F.pure(v) case Task.Error(e) => F.raiseError(e) case Task.Eval(thunk) => F.delay(thunk()) case Task.Suspend(thunk) => F.suspend(to(thunk())) case other => suspend(other)(F) }
这个Suspend结构就是延迟运算的核心。monix.Task是一套新出现的解决方案，借鉴了许多scalaz.Task的概念和方法同时又加入了很多优化、附加的功能，并且github更新也很近期。使用monix.Task应该是一个正确的选择。
首先我们必须解决scala.Future与monix.Task之间的转换：
import monix.eval.Task
import monix.execution.Scheduler.Implicits.global
final class FutureToTask[A](x: => Future[A]) {
def asTask: Task[A] = Task.deferFuture[A(x)
}
final class TaskToFuture[A](x: => Task[A]) {
def asFuture: Future[A] = x.runAsync
}
下面是一个完整的Task用例：
import scala.concurrent._
import scala.util._
import scala.concurrent.duration._
import monix.eval.Task
import monix.execution._
object MonixTask extends App {
import monix.execution.Scheduler.Implicits.global
// Executing a sum, which (due to the semantics of apply)
// will happen on another thread. Nothing happens on building
// this instance though, this expression is pure, being
// just a spec! Task by default has lazy behavior ;-)
val task = Task { 1 + 1 }
// Tasks get evaluated only on runAsync!
// Callback style:
val cancelable = task.runOnComplete {
case Success(value) =>
println(value)
case Failure(ex) =>
System.out.println(s"ERROR: ${ex.getMessage}")
}
//=> 2
// If we change our mind...
cancelable.cancel()
// Or you can convert it into a Future
val future: CancelableFuture[Int] =
task.runAsync
// Printing the result asynchronously
future.foreach(println)
//=> 2
val task = Task.now { println("Effect"); "Hello!" }
//=> Effect
// task: monix.eval.Task[String] = Delay(Now(Hello!))
}
下面我们就看看各种Task的构建方法：
/* ------ taskNow ----*/
val taskNow = Task.now { println("Effect"); "Hello!" }
//=> Effect
// taskNow: monix.eval.Task[String] = Delay(Now(Hello!))
/* --------taskDelay possible another on thread ------*/
val taskDelay = Task { println("Effect"); "Hello!" }
// taskDelay: monix.eval.Task[String] = Delay(Always(<function0>))
taskDelay.runAsync.foreach(println)
//=> Effect
//=> Hello!
// The evaluation (and thus all contained side effects)
// gets triggered on each runAsync:
taskDelay.runAsync.foreach(println)
//=> Effect
//=> Hello!
/* --------taskOnce ------- */
val taskOnce = Task.evalOnce { println("Effect"); "Hello!" }
// taskOnce: monix.eval.Task[String] = EvalOnce(<function0>)
taskOnce.runAsync.foreach(println)
//=> Effect
//=> Hello!
// Result was memoized on the first run!
taskOnce.runAsync.foreach(println)
//=> Hello!
/* --------taskFork ------- */
// this guarantees that our task will get executed asynchronously:
val task = Task(Task.eval("Hello!")).executeAsync
//val task = Task.fork(Task.eval("Hello!"))
// The default scheduler
import monix.execution.Scheduler.Implicits.global
// Creating a special scheduler meant for I/O
import monix.execution.Scheduler
lazy val io = Scheduler.io(name="my-io")
//Then we can manage what executes on which:
// Override the default Scheduler by fork:
val source = Task(println(s"Running on thread: ${Thread.currentThread.getName}"))
val forked = source.executeOn(io,true)
// val forked = Task.fork(source, io)
source.runAsync
//=> Running on thread: ForkJoinPool-1-worker-1
forked.runAsync
//=> Running on thread: my-io-4
/* --------taskError ------- */
import scala.concurrent.TimeoutException
val taskError = Task.raiseError[Int](new TimeoutException)
// error: monix.eval.Task[Int] =
// Delay(Error(java.util.concurrent.TimeoutException))
taskError.runOnComplete(result => println(result))
//=> Failure(java.util.concurrent.TimeoutException)
下面是一些控制函数：
final def doOnFinish(f: Option[Throwable] => Task[Unit]): Task[A] =
final def doOnCancel(callback: Task[Unit]): Task[A] =
final def onCancelRaiseError(e: Throwable): Task[A] =
final def onErrorRecoverWith[B >: A](pf: PartialFunction[Throwable, Task[B]]): Task[B] =
final def onErrorHandleWith[B >: A](f: Throwable => Task[B]): Task[B] =
final def onErrorFallbackTo[B >: A](that: Task[B]): Task[B] =
final def restartUntil(p: (A) => Boolean): Task[A] =
final def onErrorRestart(maxRetries: Long): Task[A] =
final def onErrorRestartIf(p: Throwable => Boolean): Task[A] =
final def onErrorRestartLoop[S, B >: A](initial: S)(f: (Throwable, S, S => Task[B]) => Task[B]): Task[B] =
final def onErrorHandle[U >: A](f: Throwable => U): Task[U] =
final def onErrorRecover[U >: A](pf: PartialFunction[Throwable, U]): Task[U] =
Task是通过asyncRun和runSync来进行异步、同步实际运算的：
def runAsync(implicit s: Scheduler): CancelableFuture[A] =
def runAsync(cb: Callback[A])(implicit s: Scheduler): Cancelable =
def runAsyncOpt(implicit s: Scheduler, opts: Options): CancelableFuture[A] =
def runAsyncOpt(cb: Callback[A])(implicit s: Scheduler, opts: Options): Cancelable =
final def runSyncMaybe(implicit s: Scheduler): Either[CancelableFuture[A], A] =
final def runSyncMaybeOpt(implicit s: Scheduler, opts: Options): Either[CancelableFuture[A], A] =
final def runSyncUnsafe(timeout: Duration)
(implicit s: Scheduler, permit: CanBlock): A =
final def runSyncUnsafeOpt(timeout: Duration)
(implicit s: Scheduler, opts: Options, permit: CanBlock): A =
final def runOnComplete(f: Try[A] => Unit)(implicit s: Scheduler): Cancelable =
下面示范了两个通常的Task运算方法：
val task1 = Task {println("sum:"); 1+2}.delayExecution(1 second)
println(task1.runSyncUnsafe(2 seconds))
task1.runOnComplete {
case Success(r) => println(s"result: $r")
case Failure(e) => println(e.getMessage)
}
下面是本次示范的源代码：
import scala.util._
import scala.concurrent.duration._
import monix.eval.Task
import monix.execution._
object MonixTask extends App {
import monix.execution.Scheduler.Implicits.global
// Executing a sum, which (due to the semantics of apply)
// will happen on another thread. Nothing happens on building
// this instance though, this expression is pure, being
// just a spec! Task by default has lazy behavior ;-)
val task = Task { 1 + 1 }
// Tasks get evaluated only on runAsync!
// Callback style:
val cancelable = task.runOnComplete {
case Success(value) =>
println(value)
case Failure(ex) =>
System.out.println(s"ERROR: ${ex.getMessage}")
}
//=> 2
// If we change our mind...
cancelable.cancel()
// Or you can convert it into a Future
val future: CancelableFuture[Int] =
task.runAsync
// Printing the result asynchronously
future.foreach(println)
//=> 2
/* ------ taskNow ----*/
val taskNow = Task.now { println("Effect"); "Hello!" }
//=> Effect
// taskNow: monix.eval.Task[String] = Delay(Now(Hello!))
/* --------taskDelay possible another on thread ------*/
val taskDelay = Task { println("Effect"); "Hello!" }
// taskDelay: monix.eval.Task[String] = Delay(Always(<function0>))
taskDelay.runAsync.foreach(println)
//=> Effect
//=> Hello!
// The evaluation (and thus all contained side effects)
// gets triggered on each runAsync:
taskDelay.runAsync.foreach(println)
//=> Effect
//=> Hello!
/* --------taskOnce ------- */
val taskOnce = Task.evalOnce { println("Effect"); "Hello!" }
// taskOnce: monix.eval.Task[String] = EvalOnce(<function0>)
taskOnce.runAsync.foreach(println)
//=> Effect
//=> Hello!
// Result was memoized on the first run!
taskOnce.runAsync.foreach(println)
//=> Hello!
/* --------taskFork ------- */
// this guarantees that our task will get executed asynchronously:
val task = Task(Task.eval("Hello!")).executeAsync
//val task = Task.fork(Task.eval("Hello!"))
// The default scheduler
import monix.execution.Scheduler.Implicits.global
// Creating a special scheduler meant for I/O
import monix.execution.Scheduler
lazy val io = Scheduler.io(name="my-io")
//Then we can manage what executes on which:
// Override the default Scheduler by fork:
val source = Task(println(s"Running on thread: ${Thread.currentThread.getName}"))
val forked = source.executeOn(io,true)
// val forked = Task.fork(source, io)
source.runAsync
//=> Running on thread: ForkJoinPool-1-worker-1
forked.runAsync
//=> Running on thread: my-io-4
/* --------taskError ------- */
import scala.concurrent.TimeoutException
val taskError = Task.raiseError[Int](new TimeoutException)
// error: monix.eval.Task[Int] =
// Delay(Error(java.util.concurrent.TimeoutException))
taskError.runOnComplete(result => println(result))
//=> Failure(java.util.concurrent.TimeoutException)
val task1 = Task {println("sum:"); 1+2}.delayExecution(1 second)
println(task1.runSyncUnsafe(2 seconds))
task1.runOnComplete {
case Success(r) => println(s"result: $r")
case Failure(e) => println(e.getMessage)
}
}
原创文章，作者：Maggie-Hunter，如若转载，请注明出处：https://blog.ytso.com/12804.html