Akka（25）： Stream：对接外部系统-Integration详解编程语言

   在现实应用中akka-stream往往需要集成其它的外部系统形成完整的应用。这些外部系统可能是akka系列系统或者其它类型的系统。所以，akka-stream必须提供一些函数和方法来实现与各种不同类型系统的信息交换。在这篇讨论里我们就介绍几种通用的信息交换方法和函数。

   akka-stream提供了mapAsync+ask模式可以从一个运算中的数据流向外连接某个Actor来进行数据交换。这是一种akka-stream与Actor集成的应用。说到与Actor集成，联想到如果能把akka-stream中复杂又消耗资源的运算任务交付给Actor，那么我们就可以充分利用actor模式的routing,cluster,supervison等等特殊功能来实现分布式高效安全的运算。下面就是这个mapAsync函数定义：

 /** 

 * Transform this stream by applying the given function to each of the elements 

 * as they pass through this processing step. The function returns a `Future` and the 

 * value of that future will be emitted downstream. The number of Futures 

 * that shall run in parallel is given as the first argument to ``mapAsync``. 

 * These Futures may complete in any order, but the elements that 

 * are emitted downstream are in the same order as received from upstream. 

 * If the function `f` throws an exception or if the `Future` is completed 

 * with failure and the supervision decision is [[akka.stream.Supervision.Stop]] 

 * the stream will be completed with failure. 

 * If the function `f` throws an exception or if the `Future` is completed 

 * with failure and the supervision decision is [[akka.stream.Supervision.Resume]] or 

 * [[akka.stream.Supervision.Restart]] the element is dropped and the stream continues. 

 * The function `f` is always invoked on the elements in the order they arrive. 

 * Adheres to the [[ActorAttributes.SupervisionStrategy]] attribute. 

 * Emits when the Future returned by the provided function finishes for the next element in sequence 

 * Backpressures when the number of futures reaches the configured parallelism and the downstream 

 * backpressures or the first future is not completed 

 * Completes when upstream completes and all futures have been completed and all elements have been emitted 

 * Cancels when downstream cancels 

 * @see [[#mapAsyncUnordered]] 

 def mapAsync[T](parallelism: Int)(f: Out ⇒ Future[T]): Repr[T] = via(MapAsync(parallelism, f))

mapAsync把一个函数f: Out= Future[T]在parallelism个Future里并行运算。我们来看看ask的款式：

 def ?(message: Any)(implicit timeout: Timeout, sender: ActorRef = Actor.noSender): Future[Any] = 

 internalAsk(message, timeout, sender)

刚好是 T= Future[T]这样的款式。所以我们可以用下面这种方式从Stream里与Actor沟通：

 stream.mapAsync(parallelism = 5)(elem = (ref ? elem).mapTo[String])

在以上的用例里Stream的每一个元素都通过ref ? elem发送给了ActorRef在一个Future里运算，这个Actor完成运算后返回Future[String]类型结果。值得注意的是mapAsync通过这个返回的Future来实现stream backpressure，也就是说这个运算Actor必须返回结果，否则Stream就会挂在那里了。下面我们先示范一下mapAsync的直接应用：

import akka.actor._ 

import akka.pattern._ 

import akka.stream._ 

import akka.stream.scaladsl._ 

import akka.routing._ 

import scala.concurrent.duration._ 

import akka.util.Timeout 

object StorageActor { 

 case class Query(rec: Int, qry: String) //模拟数据存写Query 

 class StorageActor extends Actor with ActorLogging { //模拟存写操作Actor 

 override def receive: Receive = { 

 case Query(num,qry) = 

 val reply = s"${self.path} is saving: [$qry]" 

 sender() ! reply //必须回复mapAsync, 抵消backpressure 

 reply 

 def props = Props(new StorageActor) 

object MapAsyncDemo extends App { 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val storageActor = sys.actorOf(StorageActor.props,"dbWriter") 


 implicit val timeout = Timeout(3 seconds) 

 Source(Stream.from(1)).delay(1.second,DelayOverflowStrategy.backpressure) 

 .mapAsync(parallelism = 3){ n = 

 (storageActor ? StorageActor.Query(n,s"Record#$n")).mapTo[String] 

 }.runWith(Sink.foreach(println)) 

 scala.io.StdIn.readLine() 

 sys.terminate() 

}

 在这个例子里parallelism=3，我们在StorageActor里把当前运算中的实例返回并显示出来：

akka://demoSys/user/dbWriter is saving: [Record#1] 

akka://demoSys/user/dbWriter is saving: [Record#2] 

akka://demoSys/user/dbWriter is saving: [Record#3] 

akka://demoSys/user/dbWriter is saving: [Record#4] 

akka://demoSys/user/dbWriter is saving: [Record#5] 

akka://demoSys/user/dbWriter is saving: [Record#6] 

...

可以看到：mapAsync只调用了一个Actor。那么所谓的并行运算parallelism=3的意思就只能代表在多个Future线程中同时运算了。为了实现对Actor模式特点的充分利用，我们可以通过router来实现在多个actor上并行运算。Router分pool和group两种类型：pool类router自己构建routees，group类型则调用已经构建的Actor。在我们这次的测试里只能使用group类型的Router，因为如果需要对routee实现监管supervision的话，pool类型的router在routee终止时会自动补充构建新的routee，如此就避开了监管策略。首先增加StorageActor的routing功能：

 val numOfActors = 3 

 val routees: List[ActorRef] = List.fill(numOfActors)( //构建3个StorageActor 

 sys.actorOf(StorageActor.props)) 

 val routeePaths: List[String] = routees.map{ref = "/user/"+ref.path.name} 

 val storageActorPool = sys.actorOf( 

 RoundRobinGroup(routeePaths).props() 

 .withDispatcher("akka.pool-dispatcher") 

 ,"starageActorPool" 

 implicit val timeout = Timeout(3 seconds) 

 Source(Stream.from(1)).delay(1.second,DelayOverflowStrategy.backpressure) 

 .mapAsync(parallelism = 1){ n = 

 (storageActorPool ? StorageActor.Query(n,s"Record#$n")).mapTo[String] 

 }.runWith(Sink.foreach(println))

我们使用了RoundRobinGroup作为智能任务分配方式。注意上面的parallelism=1：现在不需要多个Future了。再看看运行的结果显示：

akka://demoSys/user/$a is saving: [Record#1] 

akka://demoSys/user/$b is saving: [Record#2] 

akka://demoSys/user/$c is saving: [Record#3] 

akka://demoSys/user/$a is saving: [Record#4] 

akka://demoSys/user/$b is saving: [Record#5] 

akka://demoSys/user/$c is saving: [Record#6] 

akka://demoSys/user/$a is saving: [Record#7]

可以看到现在运算任务是在a,b,c三个Actor上并行运算的。既然是模拟数据库的并行存写动作，我们可以试着为每个routee增加逐步延时重启策略BackOffSupervisor：

object StorageActor { 

 case class Query(rec: Int, qry: String) //模拟数据存写Query 

 class DbException(cause: String) extends Exception(cause) //自定义存写异常 

 class StorageActor extends Actor with ActorLogging { //存写操作Actor 

 override def receive: Receive = { 

 case Query(num,qry) = 

 var res: String = "" 

 try { 

 res = saveToDB(num,qry) 

 } catch { 

 case e: Exception = Error(num,qry) //模拟操作异常 

 sender() ! res 

 case _ = 

 def saveToDB(num: Int,qry: String): String = { //模拟存写函数 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 if ( num % 3 == 0) Error(num,qry) //模拟异常 

 else { 

 log.info(s"${self.path} is saving: [$qry#$num]") 

 s"${self.path} is saving: [$qry#$num]" 

 def Error(num: Int,qry: String): String = { 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 sender() ! msg 

 throw new DbException(s"$msg blew up, boooooom!!!") 

 //验证异常重启 

 //BackoffStrategy.onStop goes through restart process 

 override def preRestart(reason: Throwable, message: Option[Any]): Unit = { 

 log.info(s"Restarting ${self.path.name} on ${reason.getMessage}") 

 super.preRestart(reason, message) 

 override def postRestart(reason: Throwable): Unit = { 

 log.info(s"Restarted ${self.path.name} on ${reason.getMessage}") 

 super.postRestart(reason) 

 override def postStop(): Unit = { 

 log.info(s"Stopped ${self.path.name}!") 

 super.postStop() 

 //BackOffStrategy.onFailure dosnt go through restart process 

 override def preStart(): Unit = { 

 log.info(s"PreStarting ${self.path.name} ...") 

 super.preStart() 


object StorageActorGuardian { //带监管策略的StorageActor 

 def props: Props = { //在这里定义了监管策略和StorageActor构建 

 def decider: PartialFunction[Throwable, SupervisorStrategy.Directive] = { 

 case _: StorageActor.DbException = SupervisorStrategy.Restart 

 val options = Backoff.onStop(StorageActor.props, "dbWriter", 100 millis, 500 millis, 0.0) 

 .withManualReset 

 .withSupervisorStrategy( 

 OneForOneStrategy(maxNrOfRetries = 3, withinTimeRange = 1 second)( 

 decider.orElse(SupervisorStrategy.defaultDecider) 

 BackoffSupervisor.props(options) 

object IntegrateDemo extends App { 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val numOfActors = 3 

 val routees: List[ActorRef] = List.fill(numOfActors)( 

 sys.actorOf(StorageActorGuardian.props)) 

 val routeePaths: List[String] = routees.map{ref = "/user/"+ref.path.name} //获取ActorPath 

 val storageActorPool = sys.actorOf( 

 RoundRobinGroup(routeePaths).props() 

 .withDispatcher("akka.pool-dispatcher") 

 ,"starageActorPool" 

 implicit val timeout = Timeout(3 seconds) 

 Source(Stream.from(1)).delay(3.second,DelayOverflowStrategy.backpressure) 

 .mapAsync(parallelism = 1){ n = 

 (storageActorPool ? StorageActor.Query(n,s"Record")).mapTo[String] 

 }.runWith(Sink.foreach(println)) 

 scala.io.StdIn.readLine() 

 sys.terminate() 

}

我们同时增加了模拟异常发生、StorageActor生命周期callback来跟踪异常发生时SupervisorStrategy.Restart的执行情况。从试运行反馈结果证实Backoff.onFailure不会促发Restart事件，而是直接促发了preStart事件。Backoff.onStop则走Restart过程。Backoff.onFailure是在Actor出现异常终止触动的，而Backoff.onStop则是目标Actor在任何情况下终止后触发。值得注意的是，在以上例子里运算Actor会越过造成异常的这个流元素，所以我们必须在preRestart里把这个元素补发给自己：

 //验证异常重启 

 //BackoffStrategy.onStop goes through restart process 

 override def preRestart(reason: Throwable, message: Option[Any]): Unit = { 

 log.info(s"Restarting ${self.path.name} on ${reason.getMessage}") 

 message match { 

 case Some(Query(n,qry)) = 

 self ! Query(n+101,qry) //把异常消息再补发送给自己，n+101更正了异常因素 

 case _ = 

 log.info(s"Exception message: None") 

 super.preRestart(reason, message) 

 }

如果我们不需要委托给Actor运算任务的返回结果，可以尝试用Sink.actorRefWithAck：

 /** 

 * Sends the elements of the stream to the given `ActorRef` that sends back back-pressure signal. 

 * First element is always `onInitMessage`, then stream is waiting for acknowledgement message 

 * `ackMessage` from the given actor which means that it is ready to process 

 * elements. It also requires `ackMessage` message after each stream element 

 * to make backpressure work. 

 * If the target actor terminates the stream will be canceled. 

 * When the stream is completed successfully the given `onCompleteMessage` 

 * will be sent to the destination actor. 

 * When the stream is completed with failure - result of `onFailureMessage(throwable)` 

 * function will be sent to the destination actor. 

 def actorRefWithAck[T](ref: ActorRef, onInitMessage: Any, ackMessage: Any, onCompleteMessage: Any, 

 onFailureMessage: (Throwable) ⇒ Any = Status.Failure): Sink[T, NotUsed] = 

 Sink.fromGraph(new ActorRefBackpressureSinkStage(ref, onInitMessage, ackMessage, onCompleteMessage, onFailureMessage))

在这里ActorRef只能返回有关backpressure状态信号。actorRefWithAck自己则返回Sink[T,NotUsed]，也就是说它构建了一个Sink。actorRefWithAck使用三种信号来与目标Actor沟通：

1、onInitMessage：stream发送给ActorRef的第一个信号，表示可以开始数据交换

2、ackMessage：ActorRef向stream发出的信号，回复自身准备完毕，可以接收消息，也是一种backpressure卸除消息

3、onCompleteMessage：stream发给ActorRef，通知stream已经完成了所有流元素发送

我们必须修改上个例子中的StorageActor来符合actorRefWithAck的应用和与目标Actor的沟通：

object StorageActor { 

 val onInitMessage = "start" 

 val onCompleteMessage = "done" 

 val ackMessage = "ack" 

 case class Query(rec: Int, qry: String) //模拟数据存写Query 

 class DbException(cause: String) extends Exception(cause) //自定义存写异常 

 class StorageActor extends Actor with ActorLogging { //存写操作Actor 

 override def receive: Receive = { 

 case `onInitMessage` = sender() ! ackMessage 

 case Query(num,qry) = 

 var res: String = "" 

 try { 

 res = saveToDB(num,qry) 

 } catch { 

 case e: Exception = Error(num,qry) //模拟操作异常 

 sender() ! ackMessage 

 case `onCompleteMessage` = //clean up resources 释放资源 

 case _ = 

 def saveToDB(num: Int,qry: String): String = { //模拟存写函数 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 if ( num % 5 == 0) Error(num,qry) //模拟异常 

 else { 

 log.info(s"${self.path} is saving: [$qry#$num]") 

 s"${self.path} is saving: [$qry#$num]" 

 def Error(num: Int,qry: String) = { 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 sender() ! ackMessage 

 throw new DbException(s"$msg blew up, boooooom!!!") 

 //验证异常重启 

 //BackoffStrategy.onStop goes through restart process 

 override def preRestart(reason: Throwable, message: Option[Any]): Unit = { 

 log.info(s"Restarting ${self.path.name} on ${reason.getMessage}") 

 message match { 

 case Some(Query(n,qry)) = 

 self ! Query(n+101,qry) //把异常消息再补发送给自己，n+101更正了异常因素 

 case _ = 

 log.info(s"Exception message: None") 

 super.preRestart(reason, message) 

 override def postRestart(reason: Throwable): Unit = { 

 log.info(s"Restarted ${self.path.name} on ${reason.getMessage}") 

 super.postRestart(reason) 

 override def postStop(): Unit = { 

 log.info(s"Stopped ${self.path.name}!") 

 super.postStop() 

 //BackOffStrategy.onFailure dosnt go through restart process 

 override def preStart(): Unit = { 

 log.info(s"PreStarting ${self.path.name} ...") 

 super.preStart() 

 def props = Props(new StorageActor) 

}

StorageActor类里包括了对actorRefWithAck沟通消息onInitMessage、ackMessage、onCompleteMessage的处理。这个Actor只返回backpressure消息ackMessage，而不是返回任何运算结果。注意，在preRestart里我们把造成异常的元素处理后再补发给了自己。Sink.actorRefWithAck的调用方式如下： 

 Source(Stream.from(1)).map(n = Query(n,s"Record")).delay(3.second,DelayOverflowStrategy.backpressure) 

 .runWith(Sink.actorRefWithAck( 

 storageActorPool, onInitMessage, ackMessage,onCompleteMessage))

完整的运行环境源代码如下：

object SinkActorRefWithAck extends App { 

 import StorageActor._ 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val storageActor = sys.actorOf(StorageActor.props,"storageActor") 

 val numOfActors = 3 

 val routees: List[ActorRef] = List.fill(numOfActors)( 

 sys.actorOf(StorageActorGuardian.props)) 

 val routeePaths: List[String] = routees.map{ref = "/user/"+ref.path.name} 

 val storageActorPool = sys.actorOf( 

 RoundRobinGroup(routeePaths).props() 

 .withDispatcher("akka.pool-dispatcher") 

 ,"starageActorPool" 

 Source(Stream.from(1)).map(n = Query(n,s"Record")).delay(3.second,DelayOverflowStrategy.backpressure) 

 .runWith(Sink.actorRefWithAck( 

 storageActorPool, onInitMessage, ackMessage,onCompleteMessage)) 

 scala.io.StdIn.readLine() 

 sys.terminate() 

}

如果一个外部系统向一个数据流提供数据，那我们可以把这个外部系统当作数据流的源头Source。akka-stream提供了个Source.queque函数来构建一种Source，外部系统可以利用这个Source来向Stream发送数据。Source.queque的函数款式如下：

 /** 

 * Creates a `Source` that is materialized as an [[akka.stream.scaladsl.SourceQueue]]. 

 * You can push elements to the queue and they will be emitted to the stream if there is demand from downstream, 

 * otherwise they will be buffered until request for demand is received. Elements in the buffer will be discarded 

 * if downstream is terminated. 

 * Depending on the defined [[akka.stream.OverflowStrategy]] it might drop elements if 

 * there is no space available in the buffer. 

 * Acknowledgement mechanism is available. 

 * [[akka.stream.scaladsl.SourceQueue.offer]] returns `Future[QueueOfferResult]` which completes with 

 * `QueueOfferResult.Enqueued` if element was added to buffer or sent downstream. It completes with 

 * `QueueOfferResult.Dropped` if element was dropped. Can also complete with `QueueOfferResult.Failure` - 

 * when stream failed or `QueueOfferResult.QueueClosed` when downstream is completed. 

 * The strategy [[akka.stream.OverflowStrategy.backpressure]] will not complete last `offer():Future` 

 * call when buffer is full. 

 * You can watch accessibility of stream with [[akka.stream.scaladsl.SourceQueue.watchCompletion]]. 

 * It returns future that completes with success when stream is completed or fail when stream is failed. 

 * The buffer can be disabled by using `bufferSize` of 0 and then received message will wait 

 * for downstream demand unless there is another message waiting for downstream demand, in that case 

 * offer result will be completed according to the overflow strategy. 

 * @param bufferSize size of buffer in element count 

 * @param overflowStrategy Strategy that is used when incoming elements cannot fit inside the buffer 

 def queue[T](bufferSize: Int, overflowStrategy: OverflowStrategy): Source[T, SourceQueueWithComplete[T]] = 

 Source.fromGraph(new QueueSource(bufferSize, overflowStrategy).withAttributes(DefaultAttributes.queueSource))

Source.queue构建了一个Source：Source[T,SourceQueueWithComplete[T]]，SourceQueueWithComplete类型如下：

/** 

 * This trait adds completion support to [[SourceQueue]]. 

trait SourceQueueWithComplete[T] extends SourceQueue[T] { 

 /** 

 * Complete the stream normally. Use `watchCompletion` to be notified of this 

 * operation’s success. 

 def complete(): Unit 

 /** 

 * Complete the stream with a failure. Use `watchCompletion` to be notified of this 

 * operation’s success. 

 def fail(ex: Throwable): Unit 

 /** 

 * Method returns a [[Future]] that will be completed if the stream completes, 

 * or will be failed when the stage faces an internal failure or the the [[SourceQueueWithComplete.fail]] method is invoked. 

 def watchCompletion(): Future[Done] 

}

它在SourceQueue的基础上增加了几个抽象函数，主要用来向目标数据流发送终止信号包括：complete,fail。watchCompletion是一种监视函数，返回Future代表SourceQueue被手工终止或stream由于某些原因终止运算。下面是SourceQueue定义：

/** 

 * This trait allows to have the queue as a data source for some stream. 

trait SourceQueue[T] { 

 /** 

 * Method offers next element to a stream and returns future that: 

 * - completes with `Enqueued` if element is consumed by a stream 

 * - completes with `Dropped` when stream dropped offered element 

 * - completes with `QueueClosed` when stream is completed during future is active 

 * - completes with `Failure(f)` when failure to enqueue element from upstream 

 * - fails when stream is completed or you cannot call offer in this moment because of implementation rules 

 * (like for backpressure mode and full buffer you need to wait for last offer call Future completion) 

 * @param elem element to send to a stream 

 def offer(elem: T): Future[QueueOfferResult] 

 /** 

 * Method returns a [[Future]] that will be completed if the stream completes, 

 * or will be failed when the stage faces an internal failure. 

 def watchCompletion(): Future[Done] 

}

这个界面支持了SourceQueue的基本操作：offer(elem: T), watchComplete()两个函数。下面我们就用个例子来示范SourceQueue的使用方法：我们用Calculator actor来模拟外部系统、先用Source.queue构建一个SourceQueue然后再连接下游形成一个完整的数据流。把这个数据流传给Calculator，这样Calculator就可以向这个运行中的Stream发送数据了。我们会通过这个过程来示范SourceQueue的基本操作。下面这个Calculator Actor模拟了一个外部系统作为SourceQueue用户：

object Calculator { 

 trait Operations 

 case class Add(op1:Int, op2:Int) extends Operations 

 case class DisplayError(err: Exception) extends Operations 

 case object Stop extends Operations 

 case class ProduceError(err: Exception) extends Operations 

 def props(inputQueue: SourceQueueWithComplete[String]) = Props(new Calculator(inputQueue)) 

class Calculator(inputQueue: SourceQueueWithComplete[String]) extends Actor with ActorLogging{ 

 import Calculator._ 

 import context.dispatcher 

 override def receive: Receive = { 

 case Add(op1,op2) = 

 val msg = s"$op1 + $op2 = ${op1 + op2}" 

 inputQueue.offer(msg) //.mapTo[String] 

 .recover { 

 case e: Exception = DisplayError(e)} 

 .pipeTo(self) 

 case QueueOfferResult.Enqueued = 

 log.info("QueueOfferResult.Enqueued") 

 case QueueOfferResult.Dropped = 

 case QueueOfferResult.Failure(cause) = 

 case QueueOfferResult.QueueClosed = 

 log.info("QueueOfferResult.QueueClosed") 

 case Stop = inputQueue.complete() 

 case ProduceError(e) = inputQueue.fail(e) 

}

我们看到，Calculator通过传入的inputQueue把计算结果传给数据流显示出来。在receive函数里我们把offer用法以及它可能产生的返回结果通过pipeTo都做了示范。注意：不能使用mapTo[String]，因为offer返回Future[T]，T不是String，会造成类型转换错误。而我们已经在Source.queue[String]注明了offer(elem) elem的类型是String。inputQueue的构建方式如下：

 val source: Source[String, SourceQueueWithComplete[String]] = 

 Source.queue[String](bufferSize = 16, 

 overflowStrategy = OverflowStrategy.backpressure) 

 val inputQueue: SourceQueueWithComplete[String] = source.toMat(Sink.foreach(println))(Keep.left).run() 

 inputQueue.watchCompletion().onComplete { 

 case Success(result) = println(s"Calculator ends with: $result") 

 case Failure(cause) = println(s"Calculator ends with exception: ${cause.getMessage}") 

 }

增加了watchCompetion来监测SourceQueue发出的终止信号。我们还可以看到：以上SoureQueue实例source是支持backpressure的。下面是这个例子的具体运算方式：

object SourceQueueDemo extends App { 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val source: Source[String, SourceQueueWithComplete[String]] = 

 Source.queue[String](bufferSize = 16, 

 overflowStrategy = OverflowStrategy.backpressure) 

 val inputQueue: SourceQueueWithComplete[String] = source.toMat(Sink.foreach(println))(Keep.left).run() 

 inputQueue.watchCompletion().onComplete { 

 case Success(result) = println(s"Calculator ends with: $result") 

 case Failure(cause) = println(s"Calculator ends with exception: ${cause.getMessage}") 

 val calc = sys.actorOf(Calculator.props(inputQueue),"calculator") 

 import Calculator._ 

 calc ! Add(3,5) 

 scala.io.StdIn.readLine 

 calc ! Add(39,1) 

 scala.io.StdIn.readLine 

 calc ! ProduceError(new Exception("Boooooommm!")) 

 scala.io.StdIn.readLine 

 calc ! Add(1,1) 

 scala.io.StdIn.readLine 

 sys.terminate() 

}

在本次讨论里我们了解了akka-stream与外界系统对接集成的一些情况。主要介绍了一些支持Reactive-Stream backpressure的方法。

以下是本次示范的全部源代码：

MapAsyncDemo.scala:

import akka.actor._ 

import akka.pattern._ 

import akka.stream._ 

import akka.stream.scaladsl._ 

import akka.routing._ 

import scala.concurrent.duration._ 

import akka.util.Timeout 

object StorageActor { 

 case class Query(rec: Int, qry: String) //模拟数据存写Query 

 class DbException(cause: String) extends Exception(cause) //自定义存写异常 

 class StorageActor extends Actor with ActorLogging { //存写操作Actor 

 override def receive: Receive = { 

 case Query(num,qry) = 

 var res: String = "" 

 try { 

 res = saveToDB(num,qry) 

 } catch { 

 case e: Exception = Error(num,qry) //模拟操作异常 

 sender() ! res 

 case _ = 

 def saveToDB(num: Int,qry: String): String = { //模拟存写函数 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 if ( num % 5 == 0) Error(num,qry) //模拟异常 

 else { 

 log.info(s"${self.path} is saving: [$qry#$num]") 

 s"${self.path} is saving: [$qry#$num]" 

 def Error(num: Int,qry: String): String = { 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 sender() ! msg //卸去backpressure 

 throw new DbException(s"$msg blew up, boooooom!!!") 

 //验证异常重启 

 //BackoffStrategy.onStop goes through restart process 

 override def preRestart(reason: Throwable, message: Option[Any]): Unit = { 

 log.info(s"Restarting ${self.path.name} on ${reason.getMessage}") 

 message match { 

 case Some(Query(n,qry)) = 

 self ! Query(n+101,qry) //把异常消息再补发送给自己，n+101更正了异常因素 

 case _ = 

 log.info(s"Exception message: None") 

 super.preRestart(reason, message) 

 override def postRestart(reason: Throwable): Unit = { 

 log.info(s"Restarted ${self.path.name} on ${reason.getMessage}") 

 super.postRestart(reason) 

 override def postStop(): Unit = { 

 log.info(s"Stopped ${self.path.name}!") 

 super.postStop() 

 //BackOffStrategy.onFailure dosnt go through restart process 

 override def preStart(): Unit = { 

 log.info(s"PreStarting ${self.path.name} ...") 

 super.preStart() 

 def props = Props(new StorageActor) 

object StorageActorGuardian { //带监管策略的StorageActor 

 def props: Props = { //在这里定义了监管策略和StorageActor构建 

 def decider: PartialFunction[Throwable, SupervisorStrategy.Directive] = { 

 case _: StorageActor.DbException = SupervisorStrategy.Restart 

 val options = Backoff.onStop(StorageActor.props, "dbWriter", 100 millis, 500 millis, 0.0) 

 .withManualReset 

 .withSupervisorStrategy( 

 OneForOneStrategy(maxNrOfRetries = 3, withinTimeRange = 1 second)( 

 decider.orElse(SupervisorStrategy.defaultDecider) 

 BackoffSupervisor.props(options) 

object IntegrateDemo extends App { 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val numOfActors = 3 

 val routees: List[ActorRef] = List.fill(numOfActors)( 

 sys.actorOf(StorageActorGuardian.props)) 

 val routeePaths: List[String] = routees.map{ref = "/user/"+ref.path.name} 

 val storageActorPool = sys.actorOf( 

 RoundRobinGroup(routeePaths).props() 

 .withDispatcher("akka.pool-dispatcher") 

 ,"starageActorPool" 

 implicit val timeout = Timeout(3 seconds) 

 Source(Stream.from(1)).delay(3.second,DelayOverflowStrategy.backpressure) 

 .mapAsync(parallelism = 1){ n = 

 (storageActorPool ? StorageActor.Query(n,s"Record")).mapTo[String] 

 }.runWith(Sink.foreach(println)) 

 scala.io.StdIn.readLine() 

 sys.terminate() 

}

SinkActorRefAckDemo.scala:

package sinkactorrefack 

import akka.actor._ 

import akka.pattern._ 

import akka.stream._ 

import akka.stream.scaladsl._ 

import akka.routing._ 

import scala.concurrent.duration._ 

object StorageActor { 

 val onInitMessage = "start" 

 val onCompleteMessage = "done" 

 val ackMessage = "ack" 

 case class Query(rec: Int, qry: String) //模拟数据存写Query 

 class DbException(cause: String) extends Exception(cause) //自定义存写异常 

 class StorageActor extends Actor with ActorLogging { //存写操作Actor 

 override def receive: Receive = { 

 case `onInitMessage` = sender() ! ackMessage 

 case Query(num,qry) = 

 var res: String = "" 

 try { 

 res = saveToDB(num,qry) 

 } catch { 

 case e: Exception = Error(num,qry) //模拟操作异常 

 sender() ! ackMessage 

 case `onCompleteMessage` = //clean up resources 释放资源 

 case _ = 

 def saveToDB(num: Int,qry: String): String = { //模拟存写函数 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 if ( num == 3) Error(num,qry) //模拟异常 

 else { 

 log.info(s"${self.path} is saving: [$qry#$num]") 

 s"${self.path} is saving: [$qry#$num]" 

 def Error(num: Int,qry: String) = { 

 val msg = s"${self.path} is saving: [$qry#$num]" 

 sender() ! ackMessage 

 throw new DbException(s"$msg blew up, boooooom!!!") 

 //验证异常重启 

 //BackoffStrategy.onStop goes through restart process 

 override def preRestart(reason: Throwable, message: Option[Any]): Unit = { 

 log.info(s"Restarting ${self.path.name} on ${reason.getMessage}") 

 message match { 

 case Some(Query(n,qry)) = 

 self ! Query(n+101,qry) //把异常消息再补发送给自己，n+101更正了异常因素 

 case _ = 

 log.info(s"Exception message: None") 

 super.preRestart(reason, message) 

 override def postRestart(reason: Throwable): Unit = { 

 log.info(s"Restarted ${self.path.name} on ${reason.getMessage}") 

 super.postRestart(reason) 

 override def postStop(): Unit = { 

 log.info(s"Stopped ${self.path.name}!") 

 super.postStop() 

 //BackOffStrategy.onFailure dosnt go through restart process 

 override def preStart(): Unit = { 

 log.info(s"PreStarting ${self.path.name} ...") 

 super.preStart() 

 def props = Props(new StorageActor) 

object StorageActorGuardian { //带监管策略的StorageActor 

 def props: Props = { //在这里定义了监管策略和StorageActor构建 

 def decider: PartialFunction[Throwable, SupervisorStrategy.Directive] = { 

 case _: StorageActor.DbException = SupervisorStrategy.Restart 

 val options = Backoff.onStop(StorageActor.props, "dbWriter", 100 millis, 500 millis, 0.0) 

 .withManualReset 

 .withSupervisorStrategy( 

 OneForOneStrategy(maxNrOfRetries = 3, withinTimeRange = 1 second)( 

 decider.orElse(SupervisorStrategy.defaultDecider) 

 BackoffSupervisor.props(options) 

object SinkActorRefWithAck extends App { 

 import StorageActor._ 

 implicit val sys = ActorSystem("demoSys") 

 implicit val ec = sys.dispatcher 

 implicit val mat = ActorMaterializer( 

 ActorMaterializerSettings(sys) 

 .withInputBuffer(initialSize = 16, maxSize = 16) 

 val storageActor = sys.actorOf(StorageActor.props,"storageActor") 

 val numOfActors = 3 

 val routees: List[ActorRef] = List.fill(numOfActors)( 

 sys.actorOf(StorageActorGuardian.props)) 

 val routeePaths: List[String] = routees.map{ref = "/user/"+ref.path.name} 

 val storageActorPool = sys.actorOf( 

 RoundRobinGroup(routeePaths).props() 

 .withDispatcher("akka.pool-dispatcher") 

 ,"starageActorPool" 

 Source(Stream.from(1)).map(n = Query(n,s"Record")).delay(3.second,DelayOverflowStrategy.backpressure) 

 .runWith(Sink.actorRefWithAck( 

 storageActorPool, onInitMessage, ackMessage,onCompleteMessage)) 

 scala.io.StdIn.readLine() 

 sys.terminate() 

}

SourceQueueDemo.scala：

import akka.actor._ 

import akka.stream._ 

import akka.stream.scaladsl._ 

import scala.concurrent._ 

import scala.util._ 

import akka.pattern._ 

object Calculator { 

 trait Operations 

 case class Add(op1:Int, op2:Int) extends Operations 

 case class DisplayError(err: Exception) extends Operations 

 case object Stop extends Operations 

 case class ProduceError(err: Exception) extends Operations 

 def props(inputQueue: SourceQueueWithComplete[String]) = Props(new Calculator(inputQueue)) 

class Calculator(inputQueue: SourceQueueWithComplete[String]) extends Actor with ActorLogging{ 

 import Calculator._ 

 import context.dispatcher 

 override def receive: Receive = { 

 case Add(op1,op2) = 

 val msg = s"$op1 + $op2 = ${op1 + op2}" 

 inputQueue.offer(msg) 

 .recover { 

 case e: Exception = DisplayError(e)} 

 .pipeTo(self).mapTo[String] 

 case QueueOfferResult = 

 log.info("QueueOfferResult.Enqueued") 

 case QueueOfferResult.Enqueued = 

 log.info("QueueOfferResult.Enqueued") 

 case QueueOfferResult.Dropped = 

 case QueueOfferResult.Failure(cause) = 

 case QueueOfferResult.QueueClosed = 

 log.info("QueueOfferResult.QueueClosed") 

 case Stop = inputQueue.complete() 

 case ProduceError(e) = inputQueue.fail(e) 


 val source: Source[String, SourceQueueWithComplete[String]] = 

 Source.queue[String](bufferSize = 16, 

 overflowStrategy = OverflowStrategy.backpressure) 

 val inputQueue: SourceQueueWithComplete[String] = source.toMat(Sink.foreach(println))(Keep.left).run() 

 inputQueue.watchCompletion().onComplete { 

 case Success(result) = println(s"Calculator ends with: $result") 

 case Failure(cause) = println(s"Calculator ends with exception: ${cause.getMessage}") 

 val calc = sys.actorOf(Calculator.props(inputQueue),"calculator") 

 import Calculator._ 

 calc ! Add(3,5) 

 scala.io.StdIn.readLine 

 calc ! Add(39,1) 

 scala.io.StdIn.readLine 

 calc ! ProduceError(new Exception("Boooooommm!")) 

 scala.io.StdIn.readLine 

 calc ! Add(1,1) 

 scala.io.StdIn.readLine 

 sys.terminate() 

}

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

原创文章，作者：Maggie-Hunter，如若转载，请注明出处：https://blog.ytso.com/12836.html

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