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：
1 val tnow = Task.now { println("run now ..."); 3+4 }
2 //> run now ...
3 //| tnow : scalaz.concurrent.Task[Int] = [email protected]
4 val tdelay = Task.delay { println("run delay ..."); 3+4 }
5 //> tdelay : scalaz.concurrent.Task[Int] = [email protected]
6 val tapply = Task { println("run apply ..."); 3+4 }
7 //> tapply : scalaz.concurrent.Task[Int] = [email protected]
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：
1 tnow.unsafePerformSync //> res0: Int = 7
2 tdelay.unsafePerformSync //> run delay ...
3 //| res1: Int = 7
4 tnow.unsafePerformAsync {
5 case //-(a) => println(s"the result is: $a")
6 case -//(e) => println(e.getMessage)
7 } //> the result is: 7
8 tdelay.unsafePerformAsync {
9 case //-(a) => println(s"the result is: $a")
10 case -//(e) => println(e.getMessage)
11 } //> run delay ...
12 //| the result is: 7
13 tapply.unsafePerformAsync {
14 case //-(a) => println(s"the result is: $a")
15 case -//(e) => println(e.getMessage)
16 }
17 Thread.sleep(1000) //> run apply ...
18 //| the result is: 7
从上面的例子我们可以得出：tnow已经完成了运算，因为运算结果没有”run now …”提示了。tdelay和tapply都是存在trampoline结构里的。但tapply存在更深一层的结构里，所以我们必须拖时间来等待tapply的运算结果。tdelay存放在Future.Suspend结构里，而tapply是存放在Future.Async结构里的，所以tdelay是一种延迟运算，而tapply就是异步运算了：
1 def delay[A](a: => A): Task[A] = suspend(now(a))
2 def suspend[A](a: => Task[A]): Task[A] = new Task(Future.suspend(
3 Try(a.get) match {
4 case -//(e) => Future.now(-//(e))
5 case //-(f) => f
6 }))
7 //Future.suspend:
8 def suspend[A](f: => Future[A]): Future[A] = Suspend(() => f)
9
10 def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Task[A] =
11 new Task(Future(Try(a))(pool))
12 //Future.apply
13 def apply[A](a: => A)(implicit pool: ExecutorService = Strategy.DefaultExecutorService): Future[A] = Async { cb =>
14 pool.submit { new Callable[Unit] { def call = cb(a).run }}
15 }
好了，我们再看看Task是怎样处理异常情况的：
1 def eval(value: => Int) = Task { Thread.sleep(1000); value }
2 //> eval: (value: => Int)scalaz.concurrent.Task[Int]
3 eval( 3 * 7 ).onFinish {
4 case None => Task { println("finished calculation successfully.") }
5 case Some(e) => Task { println(s"caught error [${e.getMessage}]") }
6 }.unsafePerformSyncAttempt match {
7 case -//(e) => println(s"calculation error [${e.getMessage}]")
8 case //-(a) => println(s"the result is: $a")
9 } //> finished calculation successfully.
10 //| the result is: 21
11 // 异常处理
12 eval( 3 * 7 / 0 ).onFinish {
13 case None => Task { println("finished calculation successfully.") }
14 case Some(e) => Task { println(s"caught error [${e.getMessage}]") }
15 }.unsafePerformAsync {
16 case -//(e) => println(s"calculation error [${e.getMessage}]")
17 case //-(a) => println(s"the result is: $a")
18 }
19 Thread.sleep(2000) //> caught error [/ by zero]
20 //| calculation error [/ by zero]
精准异常处理例子：
1 import java.util.concurrent._
2 val timedTask = Task {Thread.sleep(2000); 3+4}
3 //> timedTask : scalaz.concurrent.Task[Int] = [email protected]
4 timedTask.timed(1 second).handleWith {
5 case e: TimeoutException => Task { println(s"calculation exceeding time limit: ${e.getMessage}") }
6 }.unsafePerformSync //> calculation exceeding time limit: Timed out after 1000 milliseconds
7 //| res2: AnyVal{def getClass(): Class[_ >: Int with Unit <: AnyVal]} = ()
再看一些多线程编程例子：
1 val tasks = (1 |-> 5).map(n => Task{ Thread.sleep(100); n })
2 //> tasks : List[scalaz.concurrent.Task[Int]] = List([email protected]
3 //| 8b19ad, [email protected], [email protected], s
4 //| [email protected], [email protected])
5 //并行运算list of tasks
6 Task.gatherUnordered(tasks).unsafePerformSync //> res3: List[Int] = List(1, 2, 3, 4, 5)
7 val sb = new StringBuffer //> sb : StringBuffer =
8 val t1 = Task.fork { Thread.sleep(100); sb.append("a"); Task.now("a")}
9 //> t1 : scalaz.concurrent.Task[String] = [email protected]
10 val t2 = Task.fork { Thread.sleep(800); sb.append("b"); Task.now("b")}
11 //> t2 : scalaz.concurrent.Task[String] = [email protected]
12 val t3 = Task.fork { Thread.sleep(200); sb.append("c"); Task.now("c")}
13 //> t3 : scalaz.concurrent.Task[String] = [email protected]
14 val t4 = Task.fork { Thread.sleep(100); sb.append("d"); Task.now("d")}
15 //> t4 : scalaz.concurrent.Task[String] = [email protected]
16 val t5 = Task.fork { Thread.sleep(400); sb.append("e"); Task.now("e")}
17 //> t5 : scalaz.concurrent.Task[String] = [email protected]
18 val t6 = Task.fork { Thread.sleep(100); sb.append("f"); Task.now("f")}
19 //> t6 : scalaz.concurrent.Task[String] = [email protected]
20 val r = Nondeterminism[Task].nmap6(t1,t2,t3,t4,t5,t6)(List(_,_,_,_,_,_))
21 //> r : scalaz.concurrent.Task[List[String]] = [email protected]
22 r.unsafePerformSync //> res4: List[String] = List(a, b, c, d, e, f)
看个耗时算法的并行运算吧：
1 def seqFib(n: Int): Task[Int] = n match {
2 case 0 | 1 => Task now 1
3 case n => {
4 for {
5 x <- seqFib(n-1)
6 y <- seqFib(n-2)
7 } yield x + y
8 }
9 } //> seqFib: (n: Int)scalaz.concurrent.Task[Int]
10 //并行算法
11 def parFib(n: Int): Task[Int] = n match {
12 case 0 | 1 => Task now 1
13 case n => {
14 val ND = Nondeterminism[Task]
15 for {
16 pair <- ND.both(parFib(n-1), parFib(n-2))
17 (x,y) = pair
18 } yield x + y
19 }
20 } //> parFib: (n: Int)scalaz.concurrent.Task[Int]
21 def msFib(n: Int, fib: Int => Task[Int]) = for {
22 b <- Task now { System.currentTimeMillis() }
23 a <- fib(n)
24 e <- Task now { System.currentTimeMillis() }
25 } yield (a, (e-b)) //> msFib: (n: Int, fib: Int => scalaz.concurrent.Task[Int])scalaz.concurrent.T
26 //| ask[(Int, Long)]
27
28 msFib(20, parFib).unsafePerformSync //> res3: (Int, Long) = (10946,373)
29 msFib(20, 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：
1 Task {longProcess}(myExecutorService)
2 Task.fork { Task {longProcess} }(myExecutorService)
下面是一个动态指定线程池的例子：
1 import java.util.concurrent.{ExecutorService,Executors}
2 type Delegated[A] = Kleisli[Task,ExecutorService,A]
3 def delegate: Delegated[ExecutorService] = Kleisli(e => Task.now(e))
4 //> delegate: => demo.ws.task.Delegated[java.util.concurrent.ExecutorService]
5 implicit def delegateTaskToPool[A](ta: Task[A]): Delegated[A] = Kleisli(x => ta)
6 //> delegateTaskToPool: [A](ta: scalaz.concurrent.Task[A])demo.ws.task.Delegated[A]
7 val tPrg = for {
8 p <- delegate
9 b <- Task("x")(p)
10 c <- Task("y")(p)
11 } yield c //> tPrg : scalaz.Kleisli[scalaz.concurrent.Task,java.util.concurrent.Executor
12 //| Service,String] = Kleisli(<function1>)
13 tPrg.run(Executors.newFixedThreadPool(3)).unsafePerformSync
14 //> res3: String = y
当然，Task和scala Future之间是可以相互转换的：
1 import scala.concurrent.{Future => sFuture}
2 import scala.util.{Success,Failure}
3 import scala.concurrent.ExecutionContext
4 def futureToTask[A](fut: sFuture[A])(implicit ec: ExecutionContext): Task[A] =
5 Task.async {
6 cb =>
7 fut.onComplete {
8 case Success(a) => cb(a.right)
9 case Failure(e) => cb(e.left)
10 }
11 }
12 def taskToFuture[A](ta: Task[A]): sFuture[A] = {
13 val prom = scala.concurrent.Promise[A]
14 ta.unsafePerformAsync {
15 case -//(e) => prom.failure(e)
16 case //-(a) => prom.success(a)
17 }
18 prom.future
19 }
与Future不同的是：Task增加了异常处理机制。
原创文章，作者：Maggie-Hunter，如若转载，请注明出处：https://blog.ytso.com/industrynews/12903.html