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(1 to 10)  val flow = Flow[Int].map(_ * 2)  val sink = Sink.foreach(println)  val sourceGraph = GraphDSL.create(){implicit builder =>    import GraphDSL.Implicits._    val src = source.filter(_ % 2 == 0)    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(_ * 3) ~> Sink.foreach(println)     SinkShape(pipe.in)  }  val fut1 = Sink.fromGraph(sinkGraph).runWith(source)  Thread.sleep(1000)  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.immutablecase 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(1 to 10)      val sink = Sink.foreach(println)      val f: Int => Int = _ * 3      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.immutableimport scala.util.control.NonFatalobject 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(1 to 10)      val sink = Sink.foreach(println)      val f: Int => Int = _ * 3      val pipeShape = builder.add(new Pipe[Int,Int](f))      source ~> pipeShape.in      pipeShape.out~> sink      ClosedShape    }  ).run()  val fut = Source(1 to 10).via(applyPipe[Int,Int](_ * 2)).runForeach(println)  scala.io.StdIn.readLine()  sys.terminate()}