Akka（18）： Stream：组合数据流，组件-Graph components

akka-stream的数据流可以由一些组件组合而成。这些组件统称数据流图Graph，它描述了数据流向和处理环节。Source,Flow,Sink是最基础的Graph。用基础Graph又可以组合更复杂的复合Graph。如果一个Graph的所有端口（输入、输出）都是连接的话就是一个闭合流图RunnableGraph，否则就属于·开放流图PartialGraph。一个完整的（可运算的）数据流就是一个RunnableGraph。Graph的输出出入端口可以用Shape来描述：

/**
 * A Shape describes the inlets and outlets of a [[Graph]]. In keeping with the
 * philosophy that a Graph is a freely reusable blueprint, everything that
 * matters from the outside are the connections that can be made with it,
 * otherwise it is just a black box.
 */
abstract class Shape {
  /**
   * Scala API: get a list of all input ports
   */
  def inlets: immutable.Seq[Inlet[_]]

  /**
   * Scala API: get a list of all output ports
   */
  def outlets: immutable.Seq[Outlet[_]]

...

Shape类型的抽象函数inlets,outlets分别代表Graph形状的输入、输出端口。下面列出了aka-stream提供的几个现有形状Shape：

final case class SourceShape[+T](out: Outlet[T @uncheckedVariance]) extends Shape {...}
final case class FlowShape[-I, +O](in: Inlet[I @uncheckedVariance], out: Outlet[O @uncheckedVariance]) extends Shape {...}
final case class SinkShape[-T](in: Inlet[T @uncheckedVariance]) extends Shape {...}
sealed abstract class ClosedShape extends Shape
/**
 * A bidirectional flow of elements that consequently has two inputs and two
 * outputs, arranged like this:
 *
 * {{{
 *        +------+
 *  In1 ~>|      |~> Out1
 *        | bidi |
 * Out2 <~|      |<~ In2
 *        +------+
 * }}}
 */
final case class BidiShape[-In1, +Out1, -In2, +Out2](
  in1:  Inlet[In1 @uncheckedVariance],
  out1: Outlet[Out1 @uncheckedVariance],
  in2:  Inlet[In2 @uncheckedVariance],
  out2: Outlet[Out2 @uncheckedVariance]) extends Shape {...}
object UniformFanInShape {
  def apply[I, O](outlet: Outlet[O], inlets: Inlet[I]*): UniformFanInShape[I, O] =
    new UniformFanInShape(inlets.size, FanInShape.Ports(outlet, inlets.toList))
}
object UniformFanOutShape {
  def apply[I, O](inlet: Inlet[I], outlets: Outlet[O]*): UniformFanOutShape[I, O] =
    new UniformFanOutShape(outlets.size, FanOutShape.Ports(inlet, outlets.toList))
}

Shape是Graph类型的一个参数：

trait Graph[+S <: Shape, +M] {
  /**
   * Type-level accessor for the shape parameter of this graph.
   */
  type Shape = S @uncheckedVariance
  /**
   * The shape of a graph is all that is externally visible: its inlets and outlets.
   */
  def shape: S
...

RunnableGraph类型的Shape是ClosedShape：

/**
 * Flow with attached input and output, can be executed.
 */
final case class RunnableGraph[+Mat](override val traversalBuilder: TraversalBuilder) extends Graph[ClosedShape, Mat] {
  override def shape = ClosedShape

  /**
   * Transform only the materialized value of this RunnableGraph, leaving all other properties as they were.
   */
  def mapMaterializedValue[Mat2](f: Mat ⇒ Mat2): RunnableGraph[Mat2] =
    copy(traversalBuilder.transformMat(f.asInstanceOf[Any ⇒ Any]))

  /**
   * Run this flow and return the materialized instance from the flow.
   */
  def run()(implicit materializer: Materializer): Mat = materializer.materialize(this)
...

我们可以用akka-stream提供的GraphDSL来构建Graph。GraphDSL继承了GraphApply的create方法，GraphDSL.create(...)就是构建Graph的方法：

object GraphDSL extends GraphApply {...}
trait GraphApply {
  /**
   * Creates a new [[Graph]] by passing a [[GraphDSL.Builder]] to the given create function.
   */
  def create[S <: Shape]()(buildBlock: GraphDSL.Builder[NotUsed] ⇒ S): Graph[S, NotUsed] = {
    val builder = new GraphDSL.Builder
    val s = buildBlock(builder)

    createGraph(s, builder)
  }
...
def create[S <: Shape, Mat](g1: Graph[Shape, Mat])(buildBlock: GraphDSL.Builder[Mat] ⇒ (g1.Shape) ⇒ S): Graph[S, Mat] = {...}
def create[S <: Shape, Mat, M1, M2](g1: Graph[Shape, M1], g2: Graph[Shape, M2])(combineMat: (M1, M2) ⇒ Mat)(buildBlock: GraphDSL.Builder[Mat] ⇒ (g1.Shape, g2.Shape) ⇒ S): Graph[S, Mat] = {...}
...
def create[S <: Shape, Mat, M1, M2, M3, M4, M5](g1: Graph[Shape, M1], g2: Graph[Shape, M2], g3: Graph[Shape, M3], g4: Graph[Shape, M4], g5: Graph[Shape, M5])(combineMat: (M1, M2, M3, M4, M5) ⇒ Mat)(buildBlock: GraphDSL.Builder[Mat] ⇒ (g1.Shape, g2.Shape, g3.Shape, g4.Shape, g5.Shape) ⇒ S): Graph[S, Mat] = {
...}

buildBlock函数类型：buildBlock: GraphDSL.Builder[Mat] ⇒ (g1.Shape, g2.Shape,...,g5.Shape) ⇒ S，g?代表合并处理后的开放型流图。下面是几个最基本的Graph构建试例：

import akka.actor._
import akka.stream._
import akka.stream.scaladsl._

object SimpleGraphs extends App{

  implicit val sys = ActorSystem("streamSys")
  implicit val ec = sys.dispatcher
  implicit val mat = ActorMaterializer()

  val source = Source( to )
  val flow = Flow[Int].map(_ * )
  val sink = Sink.foreach(println)

  val sourceGraph = GraphDSL.create(){implicit builder =>
    import GraphDSL.Implicits._
    val src = source.filter(_ %  == )
    val pipe = builder.add(Flow[Int])
    src ~> pipe.in
    SourceShape(pipe.out)
  }

  Source.fromGraph(sourceGraph).runWith(sink).andThen{case _ => } // sys.terminate()}

  val flowGraph = GraphDSL.create(){implicit builder =>
    import GraphDSL.Implicits._

    val pipe = builder.add(Flow[Int])
    FlowShape(pipe.in,pipe.out)
  }

  val (_,fut) = Flow.fromGraph(flowGraph).runWith(source,sink)
  fut.andThen{case _ => } //sys.terminate()}

  val sinkGraph = GraphDSL.create(){implicit builder =>
     import GraphDSL.Implicits._
     val pipe = builder.add(Flow[Int])
     pipe.out.map(_ * ) ~> Sink.foreach(println)
     SinkShape(pipe.in)
  }

  val fut1 = Sink.fromGraph(sinkGraph).runWith(source)

  Thread.sleep()
  sys.terminate()

上面我们示范了Source,Flow,Sink的Graph编写，我们使用了Flow[Int]作为共同基础组件。我们知道：akka-stream的Graph可以用更简单的Partial-Graph来组合，而所有Graph最终都是用基础流图Core-Graph如Source,Flow,Sink组合而成的。上面例子里我们是用builder.add(...)把一个Flow Graph加入到一个空的Graph模版里，builder.add返回Shape pipe用于揭露这个被加入的Graph的输入输出端口。然后我们按目标Graph的功能要求把pipe的端口连接起来就完成了这个数据流图的设计了。测试使用证明这几个Graph的功能符合预想。下面我们还可以试着自定义一种类似的Pipe类型Graph来更细致的了解Graph组合的过程。所有基础组件Core-Graph都必须定义Shape来描述它的输入输出端口，定义GraphStage中的GraphStateLogic来描述对数据流元素具体的读写方式。

import akka.actor._
import akka.stream._
import akka.stream.scaladsl._
import scala.collection.immutable

case class PipeShape[In,Out](
    in: Inlet[In],
    out: Outlet[Out]) extends Shape {

  override def inlets: immutable.Seq[Inlet[_]] = in :: Nil

  override def outlets: immutable.Seq[Outlet[_]] = out :: Nil

  override def deepCopy(): Shape =
    PipeShape(
      in = in.carbonCopy(),
      out = out.carbonCopy()
    )
}

PipeShape有一个输入端口和一个输出端口。因为继承了Shape类所以必须实现Shape类的抽象函数。假设我们设计一个Graph，能把用户提供的一个函数用来对输入元素进行施用，如：source.via(ApplyPipe(myFunc)).runWith(sink)。当然，我们可以直接使用source.map(r => myFunc).runWith(sink)，不过我们需要的是：ApplyPipe里可能涉及到许多预设定的共用功能，然后myFunc是其中的一部分代码。如果用map(...)的话用户就必须提供所有的代码了。ApplyPipe的形状是PipeShape，下面是它的GraphState设计：

  class Pipe[In, Out](f: In => Out) extends GraphStage[PipeShape[In, Out]] {
    val in = Inlet[In]("Pipe.in")
    val out = Outlet[Out]("Pipe.out")

    override def shape = PipeShape(in, out)

    override def initialAttributes: Attributes = Attributes.none

    override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
      new GraphStageLogic(shape) with InHandler with OutHandler {

        private def decider =
          inheritedAttributes.get[SupervisionStrategy].map(_.decider).getOrElse(Supervision.stoppingDecider)

        override def onPull(): Unit = pull(in)

        override def onPush(): Unit = {
          try {
            push(out, f(grab(in)))
          }
          catch {
            case NonFatal(ex) ⇒ decider(ex) match {
              case Supervision.Stop ⇒ failStage(ex)
              case _ ⇒ pull(in)
            }
          }
        }

        setHandlers(in,out, this)
      }
  }

在这个Pipe GraphStage定义里首先定义了输入输出端口in,out，然后通过createLogic来定义GraphStageLogic,InHandler,outHandler。InHandler和OutHandler分别对应输入输出端口上数据元素的活动处理方式：

/**
 * Collection of callbacks for an input port of a [[GraphStage]]
 */
trait InHandler {
  /**
   * Called when the input port has a new element available. The actual element can be retrieved via the
   * [[GraphStageLogic.grab()]] method.
   */
  @throws(classOf[Exception])
  def onPush(): Unit

  /**
   * Called when the input port is finished. After this callback no other callbacks will be called for this port.
   */
  @throws(classOf[Exception])
  def onUpstreamFinish(): Unit = GraphInterpreter.currentInterpreter.activeStage.completeStage()

  /**
   * Called when the input port has failed. After this callback no other callbacks will be called for this port.
   */
  @throws(classOf[Exception])
  def onUpstreamFailure(ex: Throwable): Unit = GraphInterpreter.currentInterpreter.activeStage.failStage(ex)
}

/**
 * Collection of callbacks for an output port of a [[GraphStage]]
 */
trait OutHandler {
  /**
   * Called when the output port has received a pull, and therefore ready to emit an element, i.e. [[GraphStageLogic.push()]]
   * is now allowed to be called on this port.
   */
  @throws(classOf[Exception])
  def onPull(): Unit

  /**
   * Called when the output port will no longer accept any new elements. After this callback no other callbacks will
   * be called for this port.
   */
  @throws(classOf[Exception])
  def onDownstreamFinish(): Unit = {
    GraphInterpreter
      .currentInterpreter
      .activeStage
      .completeStage()
  }
}

akka-stream Graph的输入输出处理实现了Reactive-Stream协议。所以我们最好使用akka-stream提供现成的pull,push来重写抽象函数onPull,onPush。然后用setHandlers来设定这个GraphStage的输入输出及处理函数handler：

  /**
   * Assign callbacks for linear stage for both [[Inlet]] and [[Outlet]]
   */
  final protected def setHandlers(in: Inlet[_], out: Outlet[_], handler: InHandler with OutHandler): Unit = {
    setHandler(in, handler)
    setHandler(out, handler)
  }
 /**
   * Assigns callbacks for the events for an [[Inlet]]
   */
  final protected def setHandler(in: Inlet[_], handler: InHandler): Unit = {
    handlers(in.id) = handler
    if (_interpreter != null) _interpreter.setHandler(conn(in), handler)
  }
  /**
   * Assigns callbacks for the events for an [[Outlet]]
   */
  final protected def setHandler(out: Outlet[_], handler: OutHandler): Unit = {
    handlers(out.id + inCount) = handler
    if (_interpreter != null) _interpreter.setHandler(conn(out), handler)
  }

有了Shape和GraphStage后我们就可以构建一个Graph：

def applyPipe[In,Out](f: In => Out) = GraphDSL.create() {implicit builder =>
    val pipe = builder.add(new Pipe(f))
    FlowShape(pipe.in,pipe.out)
  }

也可以直接用来组合一个复合Graph：

  RunnableGraph.fromGraph(
    GraphDSL.create(){implicit builder =>
      import GraphDSL.Implicits._

      val source = Source( to )
      val sink = Sink.foreach(println)
      val f: Int => Int = _ *
      val pipeShape = builder.add(new Pipe[Int,Int](f))
      source ~> pipeShape.in
      pipeShape.out~> sink
      ClosedShape

    }
  ).run()

整个例子源代码如下：

import akka.actor._
import akka.stream._
import akka.stream.scaladsl._
import akka.stream.ActorAttributes._
import akka.stream.stage._

import scala.collection.immutable
import scala.util.control.NonFatal

object PipeOps {

  case class PipeShape[In, Out](
                                 in: Inlet[In],
                                 out: Outlet[Out]) extends Shape {

    override def inlets: immutable.Seq[Inlet[_]] = in :: Nil

    override def outlets: immutable.Seq[Outlet[_]] = out :: Nil

    override def deepCopy(): Shape =
      PipeShape(
        in = in.carbonCopy(),
        out = out.carbonCopy()
      )
  }

  class Pipe[In, Out](f: In => Out) extends GraphStage[PipeShape[In, Out]] {
    val in = Inlet[In]("Pipe.in")
    val out = Outlet[Out]("Pipe.out")

    override def shape = PipeShape(in, out)

    override def initialAttributes: Attributes = Attributes.none

    override def createLogic(inheritedAttributes: Attributes): GraphStageLogic =
      new GraphStageLogic(shape) with InHandler with OutHandler {

        private def decider =
          inheritedAttributes.get[SupervisionStrategy].map(_.decider).getOrElse(Supervision.stoppingDecider)

        override def onPull(): Unit = pull(in)

        override def onPush(): Unit = {
          try {
            push(out, f(grab(in)))
          }
          catch {
            case NonFatal(ex) ⇒ decider(ex) match {
              case Supervision.Stop ⇒ failStage(ex)
              case _ ⇒ pull(in)
            }
          }
        }

        setHandlers(in,out, this)
      }
  }

  def applyPipe[In,Out](f: In => Out) = GraphDSL.create() {implicit builder =>
    val pipe = builder.add(new Pipe(f))
    FlowShape(pipe.in,pipe.out)
  }

}

object ShapeDemo1 extends App {
import PipeOps._
  implicit val sys = ActorSystem("streamSys")
  implicit val ec = sys.dispatcher
  implicit val mat = ActorMaterializer()

  RunnableGraph.fromGraph(
    GraphDSL.create(){implicit builder =>
      import GraphDSL.Implicits._

      val source = Source( to )
      val sink = Sink.foreach(println)
      val f: Int => Int = _ *
      val pipeShape = builder.add(new Pipe[Int,Int](f))
      source ~> pipeShape.in
      pipeShape.out~> sink
      ClosedShape

    }
  ).run()

  val fut = Source( to ).via(applyPipe[Int,Int](_ * )).runForeach(println)

  scala.io.StdIn.readLine()

  sys.terminate()

}