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scala已经配备了自身的Future类。我们先举个例子来了解scala Future的具体操作：
1 import scala.concurrent._
2 import ExecutionContext.Implicits.global
3 object scalafuture {
4 def dbl(i: Int): Future[Int] = Future { Thread.sleep(1000) ; i + i }
5 //> dbl: (i: Int)scala.concurrent.Future[Int]
6 val fdbl = dbl(3) //> fdbl : scala.concurrent.Future[Int] = List()
7 fdbl.onSuccess {
8 case a => println(s"${a/2} + ${a/2} = $a")
9 }
10 println("calculating ...") //> calculating ...
11 Thread.sleep(2000) //> 3 + 3 = 6
12 }
这是一个标准的异步运算；在成功完成运算事件上绑定callback来获取在其它线程中的运算结果。我们也可以进行异常处理：
1 val fdz = Future { 3 / 0 } //> fdz : scala.concurrent.Future[Int] = List()
2 fdz.onFailure {
3 case e => println(s"error message {${e.getMessage}}")
4 }
5 Thread.sleep(100) //> error message {/ by zero}
又或者同时绑定运算成功和失败事件的callback函数：
1 import scala.util.{Success, Failure}
2 fdz onComplete {
3 case Success(a) => println(s"${a/2} + ${a/2} = $a")
4 case Failure(e) => println(s"error message {${e.getMessage}}")
5 }
6 Thread.sleep(100) //> error message {/ by zero}
scala Future 实现了flatMap，我们可以把几个Future组合起来用：
1 def dbl(i: Int): Future[Int] = Future { Thread.sleep(1000); i + i }
2 //> dbl: (i: Int)scala.concurrent.Future[Int]
3 def sqr(i: Int): Future[Int] = Future { i * i } //> sqr: (i: Int)scala.concurrent.Future[Int]
4 def sum(a: Int, b: Int): Future[Int] = Future { a + b }
5 //> sum: (a: Int, b: Int)scala.concurrent.Future[Int]
6 val fsum = for {
7 a <- dbl(3)
8 b <- sqr(a)
9 c <- sum(a,b)
10 } yield c //> fsum : scala.concurrent.Future[Int] = List()
11
12 fsum onSuccess { case c => println(s"the combined result is: $c") }
13 Thread.sleep(2000) //> the combined result is: 42
scala Future利用flatMap实现了流程运算：先运算dbl再sqr再sum，这个顺序是固定的即使它们可能在不同的线程里运算，因为sqr依赖dbl的结果，而sum又依赖dbl和sqr的结果。
好了，既然scala Future的功能已经比较完善了，那么scalaz的Future又有什么不同的特点呢？首先，细心一点可以发现scala Future是即时运算的，从下面的例子里可以看出：
1 import scala.concurrent.duration._
2 val fs = Future {println("run now..."); System.currentTimeMillis() }
3 //> run now...
4 //| fs : scala.concurrent.Future[Long] = List()
5 Await.result(fs, 1.second) //> res0: Long = 1465907784714
6 Thread.sleep(1000)
7 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，那么在scalaz里就必须为并发编程提供一个与scala Future具同等功能但又不会立即产生副作用的类型了，这就是scalaz版本的Future。我们看看scalaz是如何定义Future的：scalaz.concurrent/Future.scala
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]
...
Future[A]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)))
}
free structure类型可以支持算式／算法关注分离，也就是说我们可以用scalaz Future来描述程序功能而不涉及正真运算。scalaz Future的构建方式如下：
1 import scalaz._
2 import Scalaz._
3 import scalaz.concurrent._
4 import scala.concurrent.duration._
5 object scalazFuture {
6 val fnow = Future.now {println("run..."); System.currentTimeMillis()}
7 //> run...
8 //| fnow : scalaz.concurrent.Future[Long] = Now(1465909860301)
9 val fdelay = Future.delay {println("run..."); System.currentTimeMillis()}
10 //> fdelay : scalaz.concurrent.Future[Long] = Suspend(<function0>)
11 val fapply = Future {println("run..."); System.currentTimeMillis()}
12 //> fapply : scalaz.concurrent.Future[Long] = Async(<function1>)
可以看到fnow是个即时运算的构建器，而这个now就是一个lift函数, 它负责把一个普通无副作用运算升格成Future。fdelay,fapply分别把运算存入trampoline进行结构化了。我们必须另外运算trampoline来运行结构内的运算：
1 fdelay.run //> run...
2 //| res0: Long = 1465910524847
3 Thread.sleep(1000)
4 fdelay.run //> run...
5 //| res1: Long = 1465910525881
6 fapply.run //> run...
7 //| res2: Long = 1465910525883
8 Thread.sleep(1000)
9 fapply.run //> run...
10 //| res3: Long = 1465910526884
scalaz Future只有在运算时才会产生副作用，而且可以多次运算。
我们可以用即时（blocking）、异步、定时方式来运算Future：
1 fapply.unsafePerformSync //> run...
2 //| res4: Long = 1465958049118
3 fapply.unsafePerformAsync {
4 case a => println(a)
5 }
6 Thread.sleep(1000)
7 fapply.unsafePerformSyncFor(1 second) //> run...
8 //| 1465958051126
9 //| run...
10 //| res5: Long = 1465958052172
结构化状态Async代表了scalaz Future的多线程处理特性：
/**
* Create a `Future` from an asynchronous computation, which takes the form
* of a function with which we can register a callback. This can be used
* to translate from a callback-based API to a straightforward monadic
* version. See `Task.async` for a version that allows for asynchronous
* exceptions.
*/
def async[A](listen: (A => Unit) => Unit): Future[A] =
Async((cb: A => Trampoline[Unit]) => listen { a => cb(a).run })
/** Create a `Future` that will evaluate `a` using the given `ExecutorService`. */
def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Future[A] = Async { cb =>
pool.submit { new Callable[Unit] { def call = cb(a).run }}
}
/** Create a `Future` that will evaluate `a` after at least the given delay. */
def schedule[A](a: => A, delay: Duration)(implicit pool: ScheduledExecutorService =
Strategy.DefaultTimeoutScheduler): Future[A] =
Async { cb =>
pool.schedule(new Callable[Unit] {
def call = cb(a).run
}, delay.toMillis, TimeUnit.MILLISECONDS)
}
我们看到apply和schedule在构建Future时对运算线程进行了配置。
如果我们需要模仿scala Future的功效可以用unsafeStart:
1 val fs = fapply.unsafeStart //> run...
2 //| fs : scalaz.concurrent.Future[Long] = Suspend(<function0>)
3 fs.run //> res6: Long = 1465958922401
4 Thread.sleep(1000)
5 fs.run //> res7: Long = 1465958922401
我们也可以用scala Future的callback方式用async函数把自定义的callback挂在构建的Future上：
1 def fu(t: Long): Future[String] =
2 Future.async[String]{k => k(s"the curreent time is: ${t.toString}!!!")}
3 //> fu: (t: Long)scalaz.concurrent.Future[String]
4 fu(System.currentTimeMillis()).run //> res8: String = the curreent time is: 1465958923415!!!
scala Future和scalaz Future之间可以相互转换：
1 import scala.concurrent.{Future => sFuture}
2 import scala.concurrent.ExecutionContext
3 import scala.util.{Success,Failure}
4 def futureTozFuture[A](sf: sFuture[A])(implicit ec: ExecutionContext): Future[A] =
5 Future.async {cb => sf.onComplete {
6 case Success(a) => cb(a)
7 // case Failure(e) => cb(e)
8 }} //> futureTozFuture: [A](sf: scala.concurrent.Future[A])(implicit ec: scala.con
9 //| current.ExecutionContext)scalaz.concurrent.Future[A]
10 def zFutureTosFuture[A](zf: Future[A]): sFuture[A] = {
11 val prom = scala.concurrent.Promise[A]
12 zf.unsafePerformAsync {
13 case a => prom.success(a)是
14 }
15 prom.future
16 }
突然发现scalaz Future是没有异常处理（exception）功能的。scalaz提供了concurrent.Task类型填补了Future的这部分缺陷。我们会在下篇讨论Task。
我们用上面scala Future的例子来示范scalaz Future的函数组合能力：
1 def dbl(i: Int): Future[Int] = Future { i + i } //> dbl: (i: Int)scalaz.concurrent.Future[Int]
2 def sqr(i: Int): Future[Int] = Future { i * i } //> sqr: (i: Int)scalaz.concurrent.Future[Int]
3 def sum(a: Int, b: Int): Future[Int] = Future { a + b }
4 //> sum: (a: Int, b: Int)scalaz.concurrent.Future[Int]
5 val fsum = for {
6 a <- dbl(3)
7 b <- sqr(a)
8 c <- sum(a,b)
9 } yield c //> fsum : scalaz.concurrent.Future[Int] = BindAsync(<function1>,<function1>)
10
11 fsum.unsafePerformAsync {
12 case a => println(s"result c is:$a")
13 }
14 Thread.sleep(1000) //> result c is:42
Scalaz（44）－ concurrency ：scalaz Future，尚不完整的多线程类型
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原文地址：http://www.cnblogs.com/tiger-xc/p/5586834.html
