Scalaz（45）－ concurrency ：Task－函数式多线程编程核心配件

我们在上一节讨论了scalaz Future，我们说它是一个不完善的类型，最起码没有完整的异常处理机制，只能用在构建类库之类的内部环境。如果scalaz在Future类定义中增加异常处理工具的话，用户就会经常遇到Future[Throwable\/A]这样的类型，那么在进行Monadic编程时就必须使用Monad Transformer来匹配类型，程序就会变得不必要的复杂。scalaz的解决方案就是把Future[Throwable\/A]包嵌在Task类里，然后把所有Future都统一升格成Task。Task是个Monad, 这样，我们就可以统一方便地用Task来进行多线程函数式编程了。我们先看看Task的定义：scalaz.concurrent/Task.scala

class Task[+A](val get: Future[Throwable \/ A]) {

  def flatMap[B](f: A => Task[B]): Task[B] =
    new Task(get flatMap {
      case -\/(e) => Future.now(-\/(e))
      case \/-(a) => Task.Try(f(a)) match {
        case e @ -\/(_) => Future.now(e)
        case \/-(task) => task.get
      }
    })

  def map[B](f: A => B): Task[B] =
    new Task(get map { _ flatMap {a => Task.Try(f(a))} })
...

Task实现了flatMap，所以是个Monad，我们可以在for-comprehension中使用Task。

Task的构建方式与Future一样：

 val tnow = Task.now { println("run now ..."); + }
                                      //> run now ...
                                      //| tnow  : scalaz.concurrent.Task[Int] = scalaz.concurrent.Task@1a968a59
 val tdelay = Task.delay { println("run delay ...");  + }
                                     //> tdelay  : scalaz.concurrent.Task[Int] = scalaz.concurrent.Task@13deb50e
 val tapply = Task { println("run apply ..."); + }
                                    //> tapply  : scalaz.concurrent.Task[Int] = scalaz.concurrent.Task@59494225

同样，now函数是即时运算的。它就是一个lifter，能把一个普通运算直接升格为Task。

针对Task有几种运算方法：

 tnow.unsafePerformSync                            //> res0: Int = 7
 tdelay.unsafePerformSync                          //> run delay ...
                                                   //| res1: Int = 7
 tnow.unsafePerformAsync {
   case \/-(a) => println(s"the result is: $a")
   case -\/(e) => println(e.getMessage)
 }                                                 //> the result is: 7
 tdelay.unsafePerformAsync {
   case \/-(a) => println(s"the result is: $a")
   case -\/(e) => println(e.getMessage)
 }                                                 //> run delay ...
                                                   //| the result is: 7
 tapply.unsafePerformAsync {
   case \/-(a) => println(s"the result is: $a")
   case -\/(e) => println(e.getMessage)
 }
 Thread.sleep()                                //> run apply ...
                                                   //| the result is: 7

从上面的例子我们可以得出：tnow已经完成了运算，因为运算结果没有"run now ..."提示了。tdelay和tapply都是存在trampoline结构里的。但tapply存在更深一层的结构里，所以我们必须拖时间来等待tapply的运算结果。tdelay存放在Future.Suspend结构里，而tapply是存放在Future.Async结构里的，所以tdelay是一种延迟运算，而tapply就是异步运算了：

 def delay[A](a: => A): Task[A] = suspend(now(a))
 def suspend[A](a: => Task[A]): Task[A] = new Task(Future.suspend(
     Try(a.get) match {
       case -\/(e) => Future.now(-\/(e))
       case \/-(f) => f
   }))
 //Future.suspend:
  def suspend[A](f: => Future[A]): Future[A] = Suspend(() => f)

  def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Task[A] =
     new Task(Future(Try(a))(pool))
 //Future.apply
 def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Future[A] = Async { cb =>
     pool.submit { new Callable[Unit] { def call = cb(a).run }}
   }

好了，我们再看看Task是怎样处理异常情况的：

 def eval(value: => Int) = Task { Thread.sleep(); value }
                                                   //> eval: (value: => Int)scalaz.concurrent.Task[Int]
 eval(  *  ).onFinish {
   case None => Task { println("finished calculation successfully.") }
   case Some(e) => Task { println(s"caught error [${e.getMessage}]") }
 }.unsafePerformSyncAttempt match {
   case -\/(e) => println(s"calculation error [${e.getMessage}]")
   case \/-(a) => println(s"the result is: $a")
 }                                                 //> finished calculation successfully.
                                                   //| the result is: 21
 // 异常处理
 eval(  *  /  ).onFinish {
   case None => Task { println("finished calculation successfully.") }
   case Some(e) => Task { println(s"caught error [${e.getMessage}]") }
 }.unsafePerformAsync {
   case -\/(e) => println(s"calculation error [${e.getMessage}]")
   case \/-(a) => println(s"the result is: $a")
 }
 Thread.sleep()                                //> caught error [/ by zero]
                                                   //| calculation error [/ by zero]

精准异常处理例子：

 import java.util.concurrent._
 val timedTask = Task {Thread.sleep(); +}
                      //> timedTask  : scalaz.concurrent.Task[Int] = scalaz.concurrent.Task@3d921e20
 timedTask.timed( second).handleWith {
   case e: TimeoutException => Task { println(s"calculation exceeding time limit: ${e.getMessage}") }
 }.unsafePerformSync           //> calculation exceeding time limit: Timed out after 1000 milliseconds
                               //| res2: AnyVal{def getClass(): Class[_ >: Int with Unit <: AnyVal]} = ()

再看一些多线程编程例子：

 val tasks = ( |-> ).map(n => Task{ Thread.sleep(); n })
                                 //> tasks  : List[scalaz.concurrent.Task[Int]] = List(scalaz.concurrent.Task@61
                                 //| 8b19ad, scalaz.concurrent.Task@2d3379b4, scalaz.concurrent.Task@30c15d8b, s
                                 //| calaz.concurrent.Task@5e0e82ae, scalaz.concurrent.Task@6771beb3)
 //并行运算list of tasks
 Task.gatherUnordered(tasks).unsafePerformSync     //> res3: List[Int] = List(1, 2, 3, 4, 5)
 val sb = new StringBuffer                         //> sb  : StringBuffer =
 val t1 = Task.fork { Thread.sleep(); sb.append("a"); Task.now("a")}
                                  //> t1  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@6200f9cb
 val t2 = Task.fork { Thread.sleep(); sb.append("b"); Task.now("b")}
                                  //> t2  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@2002fc1d
 val t3 = Task.fork { Thread.sleep(); sb.append("c"); Task.now("c")}
                                  //> t3  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@69453e37
 val t4 = Task.fork { Thread.sleep(); sb.append("d"); Task.now("d")}
                                  //> t4  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@6f4a47c7
 val t5 = Task.fork { Thread.sleep(); sb.append("e"); Task.now("e")}
                                  //> t5  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@ae13544
 val t6 = Task.fork { Thread.sleep(); sb.append("f"); Task.now("f")}
                                  //> t6  : scalaz.concurrent.Task[String] = scalaz.concurrent.Task@3d34d211
 val r = Nondeterminism[Task].nmap6(t1,t2,t3,t4,t5,t6)(List(_,_,_,_,_,_))
                                  //> r  : scalaz.concurrent.Task[List[String]] = scalaz.concurrent.Task@394df057
 r.unsafePerformSync              //> res4: List[String] = List(a, b, c, d, e, f)

看个耗时算法的并行运算吧：

 def seqFib(n: Int): Task[Int] =  n match {
   case  |  => Task now
   case n => {
     for {
       x <- seqFib(n-)
       y <- seqFib(n-)
     } yield x + y
   }
  }                                                //> seqFib: (n: Int)scalaz.concurrent.Task[Int]
  //并行算法
  def parFib(n: Int): Task[Int] = n match {
     case  |  => Task now
     case n => {
        val ND = Nondeterminism[Task]
        for {
          pair <- ND.both(parFib(n-), parFib(n-))
          (x,y) = pair
        } yield x + y
     }
  }                                                //> parFib: (n: Int)scalaz.concurrent.Task[Int]
  def msFib(n: Int, fib: Int => Task[Int]) = for {
    b <- Task now { System.currentTimeMillis() }
    a <- fib(n)
    e <- Task now { System.currentTimeMillis() }
  } yield (a, (e-b))                               //> msFib: (n: Int, fib: Int => scalaz.concurrent.Task[Int])scalaz.concurrent.T
                                                   //| ask[(Int, Long)]

  msFib(, parFib).unsafePerformSync              //> res3: (Int, Long) = (10946,373)
  msFib(, seqFib).unsafePerformSync              //> res4: (Int, Long) = (10946,17)

哎呀！奇怪了，为什么并行算法要慢很多呢？这个问题暂且放一放，以后再研究。当然，如果有读者能给出个解释就太感激了。
Task的线程池是如何分配的呢？看看Task.apply和Task.fork：

 /** Create a `Task` that will evaluate `a` using the given `ExecutorService`. */
  def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Task[A] =
    new Task(Future(Try(a))(pool))
def fork[A](a: => Task[A])(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Task[A] =
    apply(a).join
//Future.apply
/** 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 }}
 

这两个函数都包括了一个隐式参数implicit pool: ExecutorService。默认值是Strategy.DefultExecutorService。我们可以这样指定线程池：

  Task {longProcess}(myExecutorService)
  Task.fork { Task {longProcess} }(myExecutorService)

下面是一个动态指定线程池的例子：

 import java.util.concurrent.{ExecutorService,Executors}
 type Delegated[A] = Kleisli[Task,ExecutorService,A]
 def delegate: Delegated[ExecutorService] = Kleisli(e => Task.now(e))
                             //> delegate: => demo.ws.task.Delegated[java.util.concurrent.ExecutorService]
 implicit def delegateTaskToPool[A](ta: Task[A]): Delegated[A] = Kleisli(x => ta)
                             //> delegateTaskToPool: [A](ta: scalaz.concurrent.Task[A])demo.ws.task.Delegated[A]
 val tPrg = for {
   p <- delegate
   b <- Task("x")(p)
   c <- Task("y")(p)
 } yield c                   //> tPrg  : scalaz.Kleisli[scalaz.concurrent.Task,java.util.concurrent.Executor
                             //| Service,String] = Kleisli(<function1>)
 tPrg.run(Executors.newFixedThreadPool()).unsafePerformSync
                             //> res3: String = y

当然，Task和scala Future之间是可以相互转换的：

 import scala.concurrent.{Future => sFuture}
 import scala.util.{Success,Failure}
 import scala.concurrent.ExecutionContext
 def futureToTask[A](fut: sFuture[A])(implicit ec: ExecutionContext): Task[A] =
   Task.async {
     cb =>
       fut.onComplete {
         case Success(a) => cb(a.right)
         case Failure(e) => cb(e.left)
       }
   }
 def taskToFuture[A](ta: Task[A]): sFuture[A] = {
   val prom = scala.concurrent.Promise[A]
   ta.unsafePerformAsync {
     case -\/(e) => prom.failure(e)
     case \/-(a) => prom.success(a)
   }
   prom.future
 }

与Future不同的是：Task增加了异常处理机制。