Scala 带有火花流的akka流:消息不会传递给参与者;收到死信
我是阿卡流媒体的新手。我正在从github运行下面的示例。但是发送给Helloer actor的消息没有在输出控制台中接收和显示 斯卡拉河 使用CustomerReceiverInputStream实现的程序。下面是customreceiver CustomerReceiverInputStream.scala 下面是我收到的输出死信消息Scala 带有火花流的akka流:消息不会传递给参与者;收到死信,scala,apache-spark,spark-streaming,akka-stream,akka-http,Scala,Apache Spark,Spark Streaming,Akka Stream,Akka Http,我是阿卡流媒体的新手。我正在从github运行下面的示例。但是发送给Helloer actor的消息没有在输出控制台中接收和显示 斯卡拉河 使用CustomerReceiverInputStream实现的程序。下面是customreceiver CustomerReceiverInputStream.scala 下面是我收到的输出死信消息 . . . Hello from CustomR
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Hello from CustomReceiver.START
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17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805400
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [1] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [2] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [3] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [4] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [5] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [6] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [7] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805600
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805600
[INFO] [10/10/2017 08:00:05.693] [Executor task launch worker-0] [Remoting] Remoting started; listening on addresses :[akka.tcp://streaming-actor-system-0@192.168.99.1:2552]
[INFO] [10/10/2017 08:00:05.696] [Executor task launch worker-0] [Remoting] Remoting now listens on addresses: [akka.tcp://streaming-actor-system-0@192.168.99.1:2552]
17/10/10 08:00:05 INFO ActorReceiverSupervisor: Supervision tree for receivers initialized at:akka://streaming-actor-system-0/user/Supervisor0
17/10/10 08:00:05 INFO ReceiverSupervisorImpl: Called receiver 0 onStart
17/10/10 08:00:05 INFO ReceiverSupervisorImpl: Waiting for receiver to be stopped
17/10/10 08:00:05 INFO ActorReceiverSupervisor: Started receiver worker at:akka://streaming-actor-system-0/user/Supervisor0/helloer
=== Helloer is starting up ===
=== path=akka://streaming-actor-system-0/user/Supervisor0/helloer ===
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805800
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805800
17/10/10 08:00:06 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636806000
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好的,我明白了。这里的问题是,创建作为源的Actor,helloer在另一个Actor系统中启动,并且该代码尝试通过另一个Actor系统中的akka.remote从名为SparkStreaminAkka的Actor中查找,因此使用了完整的akka.tcp url。在本规范中,它不起作用,有待进一步调查。。。但是,在本例中,不强制使用不同的ActorSystem。解决办法可以是:
import _root_.akka.actor.{Actor, Props}
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.akka.{ActorReceiver, AkkaUtils}
class Helloer extends ActorReceiver {
override def preStart() = {
println("")
println("=== Helloer is starting up ===")
println(s"=== path=${context.self.path} ===")
println("")
}
def receive = {
// store() method allows us to store the message so Spark Streaming knows about it
// This is the integration point (from Akka's side) between Spark Streaming and Akka
case s => store(s)
}
}
// Create a common actor system
object CreateActorSystem {
lazy val as = _root_.akka.actor.ActorSystem("ActorSystemSpark")
}
object StreamingApp {
import StreamingApp._
def main(args: Array[String]) {
// Configuration for a Spark application.
// Used to set various Spark parameters as key-value pairs.
val driverPort = 7777
val driverHost = "localhost"
val conf = new SparkConf()
.setMaster("local[*]") // run locally with as many threads as CPUs
.setAppName("Spark Streaming with Scala and Akka") // name in web UI
.set("spark.logConf", "true")
.set("spark.driver.port", driverPort.toString)
.set("spark.driver.host", driverHost)
val ssc = new StreamingContext(conf, Seconds(10))
val actorName = "helloer"
// This is the integration point (from Spark's side) between Spark Streaming and Akka system
// It's expected that the actor we're now instantiating will `store` messages (to close the integration loop)
// Pass actorsystem as parameter
val actorStream = AkkaUtils.createStream[String](ssc, Props[Helloer](), actorName, actorSystemCreator = () => CreateActorSystem.as)
// describe the computation on the input stream as a series of higher-level transformations
actorStream.reduce(_ + " " + _).print()
// Custom receiver
import pl.japila.spark.streaming.CustomReceiverInputDStream
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.ReceiverInputDStream
val input: ReceiverInputDStream[String] = ssc.receiverStream[String](CustomReceiverInputDStream(StorageLevel.NONE))
input.print()
// Data Ingestion from Kafka
//import org.apache.spark.streaming.kafka._
// start the streaming context so the data can be processed
// and the actor gets started
ssc.start()
// FIXME wish I knew a better way to handle the asynchrony
java.util.concurrent.TimeUnit.SECONDS.sleep(3)
import _root_.akka.actor.ActorSystem
val actorSystem = CreateActorSystem.as
//Get the actor from the path. There is no nedd o akka.remote
val helloer = actorSystem.actorSelection("/user/Supervisor0/helloer")
helloer ! "Hello"
helloer ! "from"
helloer ! "Spark Streaming"
helloer ! "with"
helloer ! "Scala"
helloer ! "and"
helloer ! "Akka"
import java.util.concurrent.TimeUnit.MINUTES
ssc.awaitTerminationOrTimeout(timeout = MINUTES.toMillis(1))
ssc.stop(stopSparkContext = true, stopGracefully = true)
}
}
这将起作用为什么不使用actorOf而不是actorSelection。根据Akka文档:actorSelection仅在消息传递时查找现有的参与者,即不创建参与者,或在创建选择时验证参与者的存在。@EmiCareOfCell44尝试使用actorOf。这次没有死信消息。但是没有按照上面代码中的说明显示发送的消息。case子句中没有任何println,您希望打印什么???@EmiCareOfCell44我期待此链接描述中提到的输出。非常感谢!您的更正正在起作用。我将获得更多关于这方面的知识,以提出akka.tcpi。我可以通过使用actorOf'来获得远程消息。但在使用actorSelection时仍面临问题`
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Hello from CustomReceiver.START
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17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805400
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [1] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [2] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [3] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [4] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [5] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [6] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
[INFO] [10/10/2017 08:00:05.475] [SparkStreamingAkka-akka.actor.default-dispatcher-6] [akka://SparkStreamingAkka/deadLetters] Message [java.lang.String] from Actor[akka://SparkStreamingAkka/deadLetters] to Actor[akka://SparkStreamingAkka/deadLetters] was not delivered. [7] dead letters encountered. This logging can be turned off or adjusted with configuration settings 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'.
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805600
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805600
[INFO] [10/10/2017 08:00:05.693] [Executor task launch worker-0] [Remoting] Remoting started; listening on addresses :[akka.tcp://streaming-actor-system-0@192.168.99.1:2552]
[INFO] [10/10/2017 08:00:05.696] [Executor task launch worker-0] [Remoting] Remoting now listens on addresses: [akka.tcp://streaming-actor-system-0@192.168.99.1:2552]
17/10/10 08:00:05 INFO ActorReceiverSupervisor: Supervision tree for receivers initialized at:akka://streaming-actor-system-0/user/Supervisor0
17/10/10 08:00:05 INFO ReceiverSupervisorImpl: Called receiver 0 onStart
17/10/10 08:00:05 INFO ReceiverSupervisorImpl: Waiting for receiver to be stopped
17/10/10 08:00:05 INFO ActorReceiverSupervisor: Started receiver worker at:akka://streaming-actor-system-0/user/Supervisor0/helloer
=== Helloer is starting up ===
=== path=akka://streaming-actor-system-0/user/Supervisor0/helloer ===
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805800
17/10/10 08:00:05 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636805800
17/10/10 08:00:06 DEBUG RecurringTimer: Callback for BlockGenerator called at time 1507636806000
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import _root_.akka.actor.{Actor, Props}
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.akka.{ActorReceiver, AkkaUtils}
class Helloer extends ActorReceiver {
override def preStart() = {
println("")
println("=== Helloer is starting up ===")
println(s"=== path=${context.self.path} ===")
println("")
}
def receive = {
// store() method allows us to store the message so Spark Streaming knows about it
// This is the integration point (from Akka's side) between Spark Streaming and Akka
case s => store(s)
}
}
// Create a common actor system
object CreateActorSystem {
lazy val as = _root_.akka.actor.ActorSystem("ActorSystemSpark")
}
object StreamingApp {
import StreamingApp._
def main(args: Array[String]) {
// Configuration for a Spark application.
// Used to set various Spark parameters as key-value pairs.
val driverPort = 7777
val driverHost = "localhost"
val conf = new SparkConf()
.setMaster("local[*]") // run locally with as many threads as CPUs
.setAppName("Spark Streaming with Scala and Akka") // name in web UI
.set("spark.logConf", "true")
.set("spark.driver.port", driverPort.toString)
.set("spark.driver.host", driverHost)
val ssc = new StreamingContext(conf, Seconds(10))
val actorName = "helloer"
// This is the integration point (from Spark's side) between Spark Streaming and Akka system
// It's expected that the actor we're now instantiating will `store` messages (to close the integration loop)
// Pass actorsystem as parameter
val actorStream = AkkaUtils.createStream[String](ssc, Props[Helloer](), actorName, actorSystemCreator = () => CreateActorSystem.as)
// describe the computation on the input stream as a series of higher-level transformations
actorStream.reduce(_ + " " + _).print()
// Custom receiver
import pl.japila.spark.streaming.CustomReceiverInputDStream
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.ReceiverInputDStream
val input: ReceiverInputDStream[String] = ssc.receiverStream[String](CustomReceiverInputDStream(StorageLevel.NONE))
input.print()
// Data Ingestion from Kafka
//import org.apache.spark.streaming.kafka._
// start the streaming context so the data can be processed
// and the actor gets started
ssc.start()
// FIXME wish I knew a better way to handle the asynchrony
java.util.concurrent.TimeUnit.SECONDS.sleep(3)
import _root_.akka.actor.ActorSystem
val actorSystem = CreateActorSystem.as
//Get the actor from the path. There is no nedd o akka.remote
val helloer = actorSystem.actorSelection("/user/Supervisor0/helloer")
helloer ! "Hello"
helloer ! "from"
helloer ! "Spark Streaming"
helloer ! "with"
helloer ! "Scala"
helloer ! "and"
helloer ! "Akka"
import java.util.concurrent.TimeUnit.MINUTES
ssc.awaitTerminationOrTimeout(timeout = MINUTES.toMillis(1))
ssc.stop(stopSparkContext = true, stopGracefully = true)
}
}