FunDA(5)- Reactive Streams:Play with Iteratees

    FunDA的设计目标就是把后台数据库中的数据搬到内存里,然后进行包括并行运算的数据处理,最后可能再对后台数据库进行更新。如果需要把数据搬到内存的话,那我们就必须考虑内存是否能一次性容纳所有的数据,有必要配合数据处理分部逐步读入,这就是Reactive Stream规范主要目的之一。所以在设计FunDA的数据源(Source)之前必须要考虑实现reacive-data-stream。Slick 3.x版在功能上的突破之一就是实现了对Reactive-Stream API的支持。遗憾的是新版的Slick并没有提供针对data-stream的具体操作函数,官方文档提到可以通过akka-stream或者Play-Iteratee-Reactive-Stream来实现对data-stream的处理操作。Slick是通过db.stream构建一个DatabasePublisher类型来实现Reactive-Stream接口的。Play则提供了stream.IterateeStreams.publisherToEnumerator(SlickDatabasePubliser)转换函数,能够把DatabasePublisher转成Reactive-Stream的数据源(Source)。Play是通过Iteratee来实现对Reactive-Stream的处理操作。我们就在这节讨论一下有关Iteratee的一些原理。在示范前我们必须在build.sbt中增加依赖:"com.typesafe.play" % "play-iteratees-reactive-streams_2.11" % "2.6.0"。所谓Reactive从字面来解释就是互动。Reacive-Stream是指数据产生方(producer)和数据使用方(consumer)之间的互动。大体上是producer通知consumer数据准备完毕可以读取、consumer通知producer读取数据的具体状态,提示是否可以发送数据。下面我们就把Reactive-Stream的基础原理给大家介绍一下:一般我们需要从一个Stream里获取数据时,可以用下面这个界面的read:

trait InputStream {
  def read(): Byte
}

这是一种典型的同步操作:read会占用线程直到获取这个Byte。我们可以用callback函数形式来解决这个问题:把一个读取函数传给目标Stream,以一种被动形式来获取这个Byte:

trait InputStreamHandler {
  def onByte(byte: Byte)
}

我们想办法把onByte传给Stream作为一种callback函数。当Stream有了Byte后调用这个onByte函数,在这个onByte函数里是收到Byte后应该进行的运算。不过收到这个Byte代表我们程序状态的一个转变,所以我们可以把上面这个界面写成函数式的:

trait InputStreamHandler {
  def onByte(byte: Byte): InputStreamHandler
}

由于状态可能转变,所以我们把当前这个有变化的对象传出来。下面是一个界面实现的例子:

class consume(data: Seq[Byte]) extends InputStreamHandler {
  def onByte(byte: Byte) = new consume(data :+ byte)
}

这个例子里我们把读取的Byte汇集到一个Seq里。但是假如Stream准备好了数据后调用我们的callback函数onByte,而我们无法立即完成函数内的运算,导致调用方线程阻塞,影响整个Stream的运转。我们可以用Future来解决这个问题:

trait InputStreamHandle {
  def onByte(byte: Byte): Future[InputStreamHandle]
}

这样调用方可以立即返回了。不过,调用方如何把数据发送状态通知数据读取方呢?比如已经完成所有数据发送。我们需要把调用方返回的数据再细化点:

trait Input[+E]
case class EL[E](e: E) extends Input[E]
case object EOF extends Input[Nothing]
case object Empty extends Input[Nothing]

现在这个返回数据是个Input[E]了,是带状态的。返回数据具体类型EL,EOF,Empty从字面就可以理解它们代表的状态了。我们的界面变成了这样:

trait InputStreamHandler[E] {
  def onInput(input: Input[E]): Future[InputStreamHandler[E]]
}

界面实现例子变成下面这样:

class consume(data: Seq[Byte]) extends InputStreamHandler[Byte] {
  def onInput(input: Input[Byte]) = input match {
     case EL(byte) => Future.successful(new consume(data :+ byte))
     case _ => Future.successful(this)
  }
}

上面这个例子中返回Future很是别扭,我们可以这样改善界面InputStreamHandler定义:

trait InputStreamHandler[E] {
  def onByte[B](cont: (Input[E] => InputStreamHandler[E]) => Future[B]): Future[B]
}

现在我们可以这样实现那个例子:

class consume(data: Seq[Byte]) extends InputStreamHandler[Byte] {
  def onByte[B](cont: (Input[Byte] => InputStreamHandler[Byte]) => Future[B]) = cont {
     case EL(byte) => new consume(data :+ byte)
     case _ => this
  }
}

现在用起来顺手多了吧。从上面这些例子中我们可以得出一种“推式”流模式(push-model-stream): 由目标stream向读取方推送数据。但Reactive-Stream应该还具备反向通告机制,比如读取方如何通知目标stream已经完成读取操作或者暂时无法再接受数据、又或者可以接受数据了。

现在我们对Reactive-Streams有了个大概的印象:这个模式由两方组成,分别是:数据源(在push-model中就是数据发送方)以及数据消耗方,分别对应了Iteratee模式的Enumerator和Iteratee。也就是说:Enumerator负责发送,Iteratee负责接收。用Iteratee实现Reactive-Streams时必须实现Enumerator和Iteratee之间的双向通告机制。实际上Iteratee描述了如何消耗Enumerator传过来的数据:比如把数据串接起来(concat)或者相加汇总等。在消耗数据的过程中Iteratee也必须负责与Enumerator沟通以保证数据传输的顺利进行。那么Iteratee又应该如何与Enumerator沟通呢?为了实现这种沟通功能,我们再设计一个trait:

trait Step[E,+A]
case class Done[+A,E](a: A, remain: Input[E]) extends Step[E,A]
case class Cont[E,+A](k: Input[E] => InputStreamHandler[E,A]) extends Step[E,A]
case class Error[E](msg: String, loc:Input[E]) extends Step[E,Nothing]

Step代表Iteratee的操作状态:Done代表完成,返回运算结果A,remain是剩余的输入、Cont代表可以用k来获取数据、Error返回错误信息msg以及出错地点loc。现在我们可以重新定义InputStreamHandler:

trait InputStreamHandler[E,A] {
  def onInput[A](step: Step[E,A] => Future[A]): Future[A]
}

界面实现例子Consume如下:

class Consume(data: Seq[Byte]) extends InputStreamHandler[Byte,Seq[Byte]] {
  def onInput(step: Step[Byte,Seq[Byte]] => Future[Seq[Byte]]) = step(Cont {
    case EL(byte) => new Consume(data :+ byte)
    case EOF => new InputStreamHandler[Byte,Seq[Byte]] {
      def onInput(step: Step[Byte,Seq[Byte]] => Future[Seq[Byte]]) = step(Done(data,Empty)) 
    }
    case Empty => this
  })
}

这个版本最大的区别在于当收到Stream发送的EOF信号后返回Done通知完成操作,可以使用运算结果data了。这个InputStreamHandler就是个Iteratee,它描述了如何使用(消耗)接收到的数据。我们可以把界面定义命名为下面这样:

trait Iteratee[E,+A] {
  def onInput[B](folder: Step[E,A] => Future[B]): Future[B]
}

实际上Iteratee模式与下面这个函数很相像:

def foldLeft[F[_],A,B](ax: F[A])(z: B)(f: (B,A) => B): B 

F[A]是个数据源,我们不需要理会它是如何产生及发送数据的,我们只关注如何去处理收到的数据。在这个函数里(B,A)=>B就是具体的数据消耗方式。foldLeft代表了一种推式流模式(push-model-stream)。至于如何产生数据源,那就是Enumerator要考虑的了。

 好了,我们先看看Iteratee正式的类型款式:Iteratee[E,A],E是数据元素类型,A是运算结果类型。trait Iteratee 有一个抽象函数:

/**
   * Computes a promised value B from the state of the Iteratee.
   *
   * The folder function will be run in the supplied ExecutionContext.
   * Exceptions thrown by the folder function will be stored in the
   * returned Promise.
   *
   * If the folder function itself is synchronous, it's better to
   * use `pureFold()` instead of `fold()`.
   *
   * @param folder a function that will be called on the current state of the iteratee
   * @param ec the ExecutionContext to run folder within
   * @return the result returned when folder is called
   */
  def fold[B](folder: Step[E, A] => Future[B])(implicit ec: ExecutionContext): Future[B]

不同功能的Iteratee就是通过定义不同的fold函数构成的。fold是个callback函数提供给Enumerator。folder的输入参数Step[E,A]代表了当前Iteratee的三种可能状态: 

object Step {
  case class Done[+A, E](a: A, remaining: Input[E]) extends Step[E, A]
  case class Cont[E, +A](k: Input[E] => Iteratee[E, A]) extends Step[E, A]
  case class Error[E](msg: String, input: Input[E]) extends Step[E, Nothing]
}

当状态为Cont[E,A]时,Enumerator就会用这个k: Input[E]=> Iteratee[E,A]函数把Input[E]推送给Iteratee。我们从一个简单的Enumerator就可以看出:

  /**
   * Creates an enumerator which produces the one supplied
   * input and nothing else. This enumerator will NOT
   * automatically produce Input.EOF after the given input.
   */
  def enumInput[E](e: Input[E]) = new Enumerator[E] {
    def apply[A](i: Iteratee[E, A]): Future[Iteratee[E, A]] =
      i.fold {
        case Step.Cont(k) => eagerFuture(k(e))
        case _ => Future.successful(i)
      }(dec)
  }

或者:

/**
   * Create an Enumerator from a set of values
   *
   * Example:
   * {{{
   *   val enumerator: Enumerator[String] = Enumerator("kiki", "foo", "bar")
   * }}}
   */
  def apply[E](in: E*): Enumerator[E] = in.length match {
    case 0 => Enumerator.empty
    case 1 => new Enumerator[E] {
      def apply[A](i: Iteratee[E, A]): Future[Iteratee[E, A]] = i.pureFoldNoEC {
        case Step.Cont(k) => k(Input.El(in.head))
        case _ => i
      }
    }
    case _ => new Enumerator[E] {
      def apply[A](i: Iteratee[E, A]): Future[Iteratee[E, A]] = enumerateSeq(in, i)
    }
  }
-----
private def enumerateSeq[E, A]: (Seq[E], Iteratee[E, A]) => Future[Iteratee[E, A]] = { (l, i) =>
    l.foldLeft(Future.successful(i))((i, e) =>
      i.flatMap(it => it.pureFold {
        case Step.Cont(k) => k(Input.El(e))
        case _ => it
      }(dec))(dec))
  }

我们可以通过定义fold函数来获取不同功能的Iteratee。下面就是一个直接返回恒量值Iteratee的定义过程:

val doneIteratee = new Iteratee[String,Int] {
   def fold[B](folder: Step[String,Int] => Future[B])(implicit ec: ExecutionContext): Future[B] = {
      folder(Step.Done(21,Input.EOF))
   }
}

这个Iteratee不会消耗任何输入,直接就返回21。实际上我们可以直接用Done.apply来构建这个doneIteratee:

val doneIteratee = Done[String,Int](21,Input.Empty)

我们也可以定义一个只消耗一个输入元素的Iteratee:

val consumeOne = new Iteratee[String,String] {
   def fold[B](folder: Step[String,String] => Future[B])(implicit ec: ExecutionContext): Future[B] = {
      folder(Step.Cont {
        case Input.EOF => Done("OK",Input.EOF)
        case Input.Empty => this
        case Input.El(e) => Done(e,Input.EOF)
      })
   }
}

同样,我们也可以用Cont构建器来构建这个consumeOne:

val consumeOne1 = Cont[String,String](in => Done("OK",Input.EOF))

从上面这些例子里我们可以推敲folder函数应该是在Enumerator里定义的,看看下面这个Enumerator例子:

val enumerator = new Enumerator[String] {
    // some messages
    val items = 1 to 10 map (i => i.toString)
    var index = 0

    override def apply[A](i: Iteratee[String, A]): 
      Future[Iteratee[String, A]] = {
      i.fold(
      // the folder
      {
        step => {
          step match {
            // iteratee is done, so no more messages
            // to send
            case Step.Done(result, remaining) => {
              println("Step.Done")
              Future(i)
            }

            // iteratee can consume more
            case Step.Cont(k: (Input[String] => Iteratee[String, A])) 
            => {
              println("Step.Cont")
              // does enumerator have more messages ?
              if (index < items.size) {
                val item = items(index)
                println(s"El($item)")
                index += 1

                // get new state of iteratee
                val newIteratee = k(Input.El(item))

                // recursive apply
                apply(newIteratee)
              } else {
                println("EOF")
                Future(k(Input.EOF))
              }
            }

            // iteratee is in error state
            case Step.Error(message, input: Input[String]) => {
              println("Step.Error")
              Future(i)
            }
          }
        }
      })
    }
  }

下面我们示范一个完整的例子: 

val userIteratee = new Iteratee[String, Unit] {
  override def fold[B](folder: (Step[String, Unit]) => Future[B])
    (implicit ec: ExecutionContext): Future[B] = {
    // accumulator
    val buffer: ListBuffer[String] = ListBuffer()

    // the step function
    def stepFn(in: Input[String]): Iteratee[String, Unit] = {
      in match {
        case Input.Empty => this
        case Input.EOF => Done({
          println(s"Result ${buffer.mkString("--")}")
        }, Input.Empty)
        case Input.El(el) => {
          buffer += el
          Cont(stepFn)
        }
      }
    }

    // initial state -> iteratee ready to accept input
    folder(Step.Cont(stepFn))
  }
}            //> userIteratee  : play.api.libs.iteratee.Iteratee[String,Unit] = demo.worksheet.iteratee2$$anonfun$main$1$$anon$3@4f063c0a
val usersEnum = Enumerator("Tiger","John","Jimmy","Kate","Chris")
            //> usersEnum  : play.api.libs.iteratee.Enumerator[String] = play.api.libs.iteratee.Enumerator$$anon$19@51cdd8a
(usersEnum |>>> userIteratee)   //> Result Tiger--John--Jimmy--Kate--Chris res0: scala.concurrent.Future[Unit] = Success(())

Enumerator usersEnum把输入推送给userIteratee、userIteratee在完成时直接把它们印了出来。在play-iterate库Iteratee对象里有个fold函数(Iteratee.fold)。这是个通用的函数,可以轻松实现上面这个userIteratee和其它的汇总功能Iteratee。Iteratee.fold函数款式如下: 

def fold[E, A](state: A)(f: (A, E) => A): Iteratee[E, A]

我们可以用这个fold函数来构建一个相似的Iteratee:

val userIteratee2 = Iteratee.fold(List[String]())((st, el:String) => st :+ el)
    //> userIteratee2  : play.api.libs.iteratee.Iteratee[String,List[String]] = Cont(<function1>)
(usersEnum |>>> userIteratee2).foreach {x => println(x)}
    //| List(Tiger, John, Jimmy, Kate, Chris)

下面是另外两个用fold函数的例子:

val inputLength: Iteratee[String,Int] = {
  Iteratee.fold[String,Int](0) { (length, chars) => length + chars.length }
           //> inputLength  : play.api.libs.iteratee.Iteratee[String,Int] = Cont(<function1>)
}
Await.result((usersEnum |>>> inputLength),Duration.Inf)
                                                  //> res1: Int = 23
val consume: Iteratee[String,String] = {
  Iteratee.fold[String,String]("") { (result, chunk) => result ++ chunk }
          //> consume  : play.api.libs.iteratee.Iteratee[String,String] = Cont(<function1 >)
}
Await.result((usersEnum |>>> consume),Duration.Inf)
                                                  //> res2: String = TigerJohnJimmyKateChris

从以上的练习里我们基本摸清了定义Iteratee的两种主要模式:

1、构建新的Iteratee,重新定义fold函数,如上面的userIteratee及下面这个上传大型json文件的例子:

object ReactiveFileUpload extends Controller {
  def upload = Action(BodyParser(rh => new CsvIteratee(isFirst = true))) {
    request =>
      Ok("File Processed")
  }
}

case class CsvIteratee(state: Symbol = 'Cont, input: Input[Array[Byte]] = Empty, lastChunk: String = "", isFirst: Boolean = false) extends Iteratee[Array[Byte], Either[Result, String]] {
  def fold[B](
               done: (Either[Result, String], Input[Array[Byte]]) => Promise[B],
               cont: (Input[Array[Byte]] => Iteratee[Array[Byte], Either[Result, String]]) => Promise[B],
               error: (String, Input[Array[Byte]]) => Promise[B]
               ): Promise[B] = state match {
    case 'Done =>
      done(Right(lastChunk), Input.Empty)

    case 'Cont => cont(in => in match {
      case in: El[Array[Byte]] => {
        // Retrieve the part that has not been processed in the previous chunk and copy it in front of the current chunk
        val content = lastChunk + new String(in.e)
        val csvBody =
          if (isFirst)
            // Skip http header if it is the first chunk
            content.drop(content.indexOf("\r\n\r\n") + 4)
          else content
        val csv = new CSVReader(new StringReader(csvBody), ';')
        val lines = csv.readAll
        // Process all lines excepted the last one since it is cut by the chunk
        for (line <- lines.init)
          processLine(line)
        // Put forward the part that has not been processed
        val last = lines.last.toList.mkString(";")
        copy(input = in, lastChunk = last, isFirst = false)
      }
      case Empty => copy(input = in, isFirst = false)
      case EOF => copy(state = 'Done, input = in, isFirst = false)
      case _ => copy(state = 'Error, input = in, isFirst = false)
    })

    case _ =>
      error("Unexpected state", input)

  }

  def processLine(line: Array[String]) = WS.url("http://localhost:9200/affa/na/").post(
    toJson(
      Map(
        "date" -> toJson(line(0)),
        "trig" -> toJson(line(1)),
        "code" -> toJson(line(2)),
        "nbjours" -> toJson(line(3).toDouble)
      )
    )
  )
}

二、直接定义Cont:

/**
   * Create an iteratee that takes the first element of the stream, if one occurs before EOF
   */
  def head[E]: Iteratee[E, Option[E]] = {

    def step: K[E, Option[E]] = {
      case Input.Empty => Cont(step)
      case Input.EOF => Done(None, Input.EOF)
      case Input.El(e) => Done(Some(e), Input.Empty)
    }
    Cont(step)
  }

及:

def fileIteratee(file: File): Iteratee[String, Long] = {
    val helper = new FileNIOHelper(file)

    def step(totalLines: Long)(in: Input[String]): Iteratee[String, Long] = in match {
      case Input.EOF | Input.Empty =>
        if(debug) println("CLOSING CHANNEL")
        helper.close()
        Done(totalLines, Input.EOF)
      case Input.El(line) =>
        if(debug) println(line)
        helper.write(line)
        Cont[String, Long](i => step(totalLines+1)(i))
    }
    // initiates iteration by initialize context and first state (Cont) and launching iteration
    Cont[String, Long](i => step(0L)(i))
  }

}

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