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FunDA(7)- Reactive Streams to fs2 Pull Streams

2019-11-11 05:25:19
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    Reactive-Stream不只是简单的push-model-stream, 它还带有“拖式”(pull-model)性质。这是因为在Iteratee模式里虽然理论上由Enumerator负责主动推送数据,实现了push-model功能。但实际上Iteratee也会根据自身情况,通过提供callback函数通知Enumerator可以开始推送数据,这从某种程度上也算是一种pull-model。换句话讲Reactive-Streams是通过push-pull-model来实现上下游Enumerator和Iteratee之间互动的。我们先看个简单的Iteratee例子:

def showElements: Iteratee[Int,Unit] = Cont {  case Input.El(e) =>     PRintln(s"EL($e)")     showElements  case Input.Empty => showElements  case Input.EOF =>     println("EOF")     Done((),Input.EOF)}                                                 //> showElements: => play.api.libs.iteratee.Iteratee[Int,Unit]val enumNumbers = Enumerator(1,2,3,4,5)           //> enumNumbers  : play.api.libs.iteratee.Enumerator[Int] = play.api.libs.iteratee.Enumerator$$anon$19@47f6473enumNumbers |>> showElements                      //> EL(1)                                                  //| EL(2)                                                  //| EL(3)                                                  //| EL(4)                                                  //| EL(5)                                                  //| res0: scala.concurrent.Future[play.api.libs.iteratee.Iteratee[Int,Unit]] = Success(Cont(<function1>))我们看到:enumNumbers |>> showElements立刻启动了运算。但并没有实际完成数据发送,因为showElements并没有收到Input.EOF。首先,我们必须用Iteratee.run来完成运算:

val it = Iteratee.flatten(enum |>> consumeAll).run//> El(1)                                                  //| El(2)                                                  //| El(3)                                                  //| El(4)                                                  //| El(5)                                                  //| El(6)                                                  //| El(7)                                                  //| El(8)                                                  //| EOF                                                  //| it  : scala.concurrent.Future[Int] = Success(99)这个run函数是这样定义的:

/**   * Extracts the computed result of the Iteratee pushing an Input.EOF if necessary   * Extracts the computed result of the Iteratee, pushing an Input.EOF first   * if the Iteratee is in the [[play.api.libs.iteratee.Cont]] state.   * In case of error, an exception may be thrown synchronously or may   * be used to complete the returned Promise; this indeterminate behavior   * is inherited from fold().   *   *  @return a [[scala.concurrent.Future]] of the eventually computed result   */  def run: Future[A] = fold({    case Step.Done(a, _) => Future.successful(a)    case Step.Cont(k) => k(Input.EOF).fold({      case Step.Done(a1, _) => Future.successful(a1)      case Step.Cont(_) => sys.error("diverging iteratee after Input.EOF")      case Step.Error(msg, e) => sys.error(msg)    })(dec)    case Step.Error(msg, e) => sys.error(msg)  })(dec)再一个问题是:enumNumbers |>> showElements是个封闭的运算,我们无法逐部分截取数据流,只能取得整个运算结果。也就是说如果我们希望把一个Enumerator产生的数据引导到fs2 Stream的话,只能在所有数据都读入内存后才能实现了。这样就违背了使用Reactive-Streams的意愿。那我们应该怎么办?一个可行的方法是使用一个存储数据结构,用两个线程,一个线程里Iteratee把当前数据存入数据结构,另一个线程里fs2把数据取出来。fs2.async.mutable包提供了个Queue类型,我们可以用这个Queue结构来作为Iteratee与fs2之间的管道:Iteratee从一头把数据压进去(enqueue),fs2从另一头把数据取出来(dequeue)。

我们先设计enqueue部分,这部分是在Iteratee里进行的:

def enqueueTofs2(q: async.mutable.Queue[Task,Option[Int]]): Iteratee[Int,Unit] = Cont {   case Input.EOF =>       q.enqueue1(None).unsafeRun       Done((),Input.EOF)   case Input.Empty => enqueueTofs2(q)   case Input.El(e) =>       q.enqueue1(Some(e)).unsafeRun       enqueueTofs2(q)}    //> enqueueTofs2: (q: fs2.async.mutable.Queue[fs2.Task,Option[Int]])play.api.libs.iteratee.Iteratee[Int,Unit]

先分析一下这个Iteratee:我们直接把enqueueTofs2放入Cont状态,也就是等待接受数据状态。当收到数据时运行q.enqueue1把数据塞入q,然后不断循环运行至收到Input.EOF。注意:q.enqueue1(Some(e)).unsafeRun是个同步运算,在未成功完成数据enqueue1的情况下会一直占用线程。所以,q另一端的dequeue部分必须是在另一个线程里运行,否则会造成整个程序的死锁。fs2的Queue类型款式是:Queue[F,A],所以我们必须用Stream.eval来对这个Queue进行函数式的操作:

val fs2Stream: Stream[Task,Int] = Stream.eval(async.boundedQueue[Task,Option[Int]](2)).flatMap { q =>    //run Enumerator-Iteratee and enqueue data in thread 1    //dequeue data and en-stream in thread 2 (current thread)  }因为Stream.eval运算结果是Stream[Task,Int],所以我们可以得出这个flatMap内的函数款式 Queue[Task,Option[Int]] => Stream[Task,Int]。下面我们先考虑如何实现数据enqueue部分:这部分是通过Iteratee的运算过程产生的。我们提到过这部分必须在另一个线程里运行,所以可以用Task来选定另一线程如下:

    Task { Iteratee.flatten(enumerator |>> pushData(q)).run }.unsafeRunAsyncFuture()现在这个Task就在后面另一个线程里自己去运算了。但它的运行进展则会依赖于另一个线程中dequeue数据的进展。我们先看看fs2提供的两个函数款式:

/** Repeatedly calls `dequeue1` forever. */  def dequeue: Stream[F, A] = Stream.bracket(cancellableDequeue1)(d => Stream.eval(d._1), d => d._2).repeat/**   * Halts the input stream at the first `None`.   *   * @example {{{   * scala> Stream[Pure, Option[Int]](Some(1), Some(2), None, Some(3), None).unNoneTerminate.toList   * res0: List[Int] = List(1, 2)   * }}}   */  def unNoneTerminate[F[_],I]: Pipe[F,Option[I],I] =    _ repeatPull { _.receive {      case (hd, tl) =>        val out = Chunk.indexedSeq(hd.toVector.takeWhile { _.isDefined }.collect { case Some(i) => i })        if (out.size == hd.size) Pull.output(out) as tl        else if (out.isEmpty) Pull.done        else Pull.output(out) >> Pull.done    }}

刚好,dequeue产生Stream[F,A]。而unNoneTerminate可以根据Stream(None)来终止运算。现在我们可以把这个Reactive-Streams到fs2-pull-streams转换过程这样来定义:

implicit val strat = Strategy.fromFixedDaemonPool(4)                                                  //> strat  : fs2.Strategy = Strategyval fs2Stream: Stream[Task,Int] = Stream.eval(async.boundedQueue[Task,Option[Int]](2)).flatMap { q =>  Task(Iteratee.flatten(enumNumbers |>> enqueueTofs2(q)).run).unsafeRunAsyncFuture  pipe.unNoneTerminate(q.dequeue)}   //> fs2Stream  : fs2.Stream[fs2.Task,Int] = attemptEval(Task).flatMap(<function1>).flatMap(<function1>)现在这个stream应该已经变成fs2.Stream[Task,Int]了。我们可以用前面的log函数来试运行一下:

def log[A](prompt: String): Pipe[Task,A,A] =    _.evalMap {row => Task.delay{ println(s"$prompt> $row"); row }}                                                  //> log: [A](prompt: String)fs2.Pipe[fs2.Task,A,A]    fs2Stream.through(log("")).run.unsafeRun          //> > 1                                                  //| > 2                                                  //| > 3                                                  //| > 4                                                  //| > 5我们成功的把Iteratee的Reactive-Stream转化成fs2的Pull-Model-Stream。

下面是这次讨论的源代码:

import play.api.libs.iteratee._import scala.concurrent._import scala.concurrent.duration._import scala.concurrent.ExecutionContext.Implicits.globalimport scala.collection.mutable._import fs2._object iteratees {def showElements: Iteratee[Int,Unit] = Cont {  case Input.El(e) =>     println(s"EL($e)")     showElements  case Input.Empty => showElements  case Input.EOF =>     println("EOF")     Done((),Input.EOF)}val enumNumbers = Enumerator(1,2,3,4,5)enumNumbers |>> showElementsIteratee.flatten(enumNumbers |>> showElements).rundef enqueueTofs2(q: async.mutable.Queue[Task,Option[Int]]): Iteratee[Int,Unit] = Cont {   case Input.EOF =>       q.enqueue1(None).unsafeRun       Done((),Input.EOF)   case Input.Empty => enqueueTofs2(q)   case Input.El(e) =>       q.enqueue1(Some(e)).unsafeRun       enqueueTofs2(q)}implicit val strat = Strategy.fromFixedDaemonPool(4)val fs2Stream: Stream[Task,Int] = Stream.eval(async.boundedQueue[Task,Option[Int]](2)).flatMap { q =>  Task(Iteratee.flatten(enumNumbers |>> enqueueTofs2(q)).run).unsafeRunAsyncFuture  pipe.unNoneTerminate(q.dequeue)}def log[A](prompt: String): Pipe[Task,A,A] =    _.evalMap {row => Task.delay{ println(s"$prompt> $row"); row }}    fs2Stream.through(log("")).run.unsafeRun }


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