此问题与the previous thread有关。我正在从用户流中提取会话＆＃39;点击活动。出于验证目的，我总是等待超时2分钟，如果用户在这2分钟内没有活动（没有点击事件），那么我认为会话已经完成。这些已完成的会话应保存在finishedSessions。
下面给出的代码提供了错误（见下文）。
settings = ssc.sparkContext.broadcast(Map(
"metadataBrokerList_OutputQueue" -> metadataBrokerList_OutputQueue,
"topicOutput" -> topicOutput))
val spec = StateSpec.function(Utils.updateState _).timeout(Minutes(2))
val latestSessionInfo = membersSessions.map[(String, (Long, Long, Long, List[String]))](a => {
//transform to (member_id, (time, time, counter, events within session))
(a._1, (a._2._1, a._2._2, 1, a._2._3))
})
val finishedSessions = latestSessionInfo.mapWithState(spec).filter(_.isDefined)
finishedSessions.foreachRDD( rdd => {
rdd.foreachPartition{iter =>
val producer = Utils.createProducer(settings.value("metadataBrokerList_OutputQueue"))
iter.foreach { msg =>
val jsonString = msg.get._4.toString()
val streamEvent = new ProducerRecord[String, String](settings.value("topicOutput"), null, jsonString)
producer.send(streamEvent)
}
producer.close()
}
})
这是我的可序列化对象Utils：
object Utils extends Serializable {
def updateState(key: String,
value: Option[(Long, Long, Long, List[String])],
state: State[(Long, Long, Long, List[String])]): Option[(Long, Long, Long, List[String])] = {
def reduce(first: (Long, Long, Long, List[String]), second: (Long, Long, Long, List[String])) = {
(Math.min(first._1, second._1), Math.max(first._2, second._2), first._3 + second._3, first._4 ++ second._4)
}
value match {
case Some(currentValue) =>
val result = state
.getOption()
.map(currentState => reduce(currentState, currentValue))
.getOrElse(currentValue)
state.update(result)
None
case _ if state.isTimingOut() => state.getOption()
}
}
def createProducer(metadataBrokerList: String): KafkaProducer[String, String] = {
val kafkaProps = new Properties()
println("metadataBrokerList: " + metadataBrokerList)
kafkaProps.put("bootstrap.servers", metadataBrokerList)
// This is mandatory, even though we don't send key
kafkaProps.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
kafkaProps.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")
kafkaProps.put("acks", "1")
// how many times to retry when produce request fails?
kafkaProps.put("retries", "3")
// This is an upper limit of how many messages Kafka Producer will attempt to batch before sending (bytes)
kafkaProps.put("batch.size", "5")
// How long will the producer wait before sending in order to allow more messages to get accumulated in the same batch
kafkaProps.put("linger.ms", "5")
new KafkaProducer[String, String](kafkaProps)
}
}
编辑：
将settings定义为本地广播变量（val settings = ...而不是settings）后，错误消息消失了。但是，出现了新的错误，它与Caused by: java.lang.ClassNotFoundException: java.time.temporal.TemporalField有关。这是什么意思？：
驱动程序堆栈跟踪：
org.apache.spark.SparkException: Job aborted due to stage failure: Task 6 in stage 3.0 failed 4 times, most recent failure: Lost task 6.3 in stage 3.0 (TID 34, ip-172-20-233-19.eu-west-1.compute.internal): java.lang.NoClassDefFoundError: java/time/temporal/TemporalField
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2$$anonfun$apply$1.apply(KafkaEventsConsumer.scala:163)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2$$anonfun$apply$1.apply(KafkaEventsConsumer.scala:160)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ClassNotFoundException: java.time.temporal.TemporalField
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
... 12 more
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2.apply(KafkaEventsConsumer.scala:160)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2.apply(KafkaEventsConsumer.scala:159)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.NoClassDefFoundError: java/time/temporal/TemporalField
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2$$anonfun$apply$1.apply(KafkaEventsConsumer.scala:163)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2$$anonfun$apply$1.apply(KafkaEventsConsumer.scala:160)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
... 3 more
Caused by: java.lang.ClassNotFoundException: java.time.temporal.TemporalField
at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
at java.security.AccessController.doPrivileged(Native Method)
at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
... 12 more
Exception in thread "streaming-job-executor-0" java.lang.Error: java.lang.InterruptedException
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1151)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.InterruptedException
at java.lang.Object.wait(Native Method)
at java.lang.Object.wait(Object.java:503)
at org.apache.spark.scheduler.JobWaiter.awaitResult(JobWaiter.scala:73)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:612)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:920)
at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1.apply(RDD.scala:918)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
at org.apache.spark.rdd.RDD.foreachPartition(RDD.scala:918)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2.apply(KafkaEventsConsumer.scala:160)
at org.testconsumer.kafka.KafkaEventsConsumer$$anonfun$run$2.apply(KafkaEventsConsumer.scala:159)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at org.apache.spark.streaming.dstream.DStream$$anonfun$foreachRDD$1$$anonfun$apply$mcV$sp$3.apply(DStream.scala:661)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:50)
at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:426)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:49)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:224)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:224)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:223)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
... 2 more
16/11/28 18:42:08 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerStageCompleted(org.apache.spark.scheduler.StageInfo@764e333a)
16/11/28 18:42:08 ERROR LiveListenerBus: SparkListenerBus has already stopped! Dropping event SparkListenerJobEnd(1,1480354928309,JobFailed(org.apache.spark.SparkException: Job 1 cancelled because SparkContext was shut down))
它似乎与Java的版本有关。我在群集上有以下版本，但是如何部署我的代码以及如何使用java.time.temporal.TemporalField来避免错误？：
java version "1.7.0_111"
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.test</groupId>
<artifactId>streaming_test</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
<java.version>1.7</java.version>
<scala.version>2.10.6</scala.version>
<spark.version>1.6.2</spark.version>
<jackson.version>2.8.3</jackson.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<!--<dependency>-->
<!--<groupId>org.apache.spark</groupId>-->
<!--<artifactId>spark-core_2.10</artifactId>-->
<!--<version>${spark.version}</version>-->
<!--</dependency>-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
<!--<dependency>-->
<!--<groupId>org.apache.spark</groupId>-->
<!--<artifactId>spark-mllib_2.10</artifactId>-->
<!--<version>${spark.version}</version>-->
<!--</dependency>-->
<dependency>
<groupId>com.fasterxml.jackson.module</groupId>
<artifactId>jackson-module-scala_2.10</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-annotations</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>${jackson.version}</version>
</dependency>
<dependency>
<groupId>net.debasishg</groupId>
<artifactId>redisclient_2.10</artifactId>
<version>3.3</version>
</dependency>
<dependency>
<groupId>org.sedis</groupId>
<artifactId>sedis_2.10</artifactId>
<version>1.2.2</version>
</dependency>
<dependency>
<groupId>com.lambdaworks</groupId>
<artifactId>jacks_2.10</artifactId>
<version>2.3.3</version>
</dependency>
<dependency>
<groupId>com.typesafe</groupId>
<artifactId>config</artifactId>
<version>1.3.1</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-aws</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>com.amazonaws</groupId>
<artifactId>aws-java-sdk-s3</artifactId>
<version>1.11.53</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>4.5.2</version>
</dependency>
<dependency>
<groupId>net.java.dev.jets3t</groupId>
<artifactId>jets3t</artifactId>
<version>0.9.4</version>
</dependency>
<!--<dependency>-->
<!--<groupId>com.github.nscala-time</groupId>-->
<!--<artifactId>nscala-time_2.10</artifactId>-->
<!--<version>2.12.0</version>-->
<!--</dependency>-->
<dependency>
<groupId>com.databricks</groupId>
<artifactId>spark-csv_2.10</artifactId>
<version>1.5.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-assembly-plugin</artifactId>
<executions>
<execution>
<id>build-a</id>
<configuration>
<archive>
<manifest>
<mainClass>org.test.consumer.SessionizerRunner</mainClass>
</manifest>
</archive>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
<finalName>myTest</finalName>
</configuration>
<phase>package</phase>
<goals>
<goal>assembly</goal>
</goals>
</execution>
</executions>
</plugin>
<!-- Configure maven-compiler-plugin to use the desired Java version -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>${java.version}</source>
<target>${java.version}</target>
</configuration>
</plugin>
<!-- Use build-helper-maven-plugin to add Scala source and test source directories -->
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<version>1.10</version>
<executions>
<execution>
<id>add-source</id>
<phase>generate-sources</phase>
<goals>
<goal>add-source</goal>
</goals>
<configuration>
<sources>
<source>src/main/scala</source>
</sources>
</configuration>
</execution>
<execution>
<id>add-test-source</id>
<phase>generate-test-sources</phase>
<goals>
<goal>add-test-source</goal>
</goals>
<configuration>
<sources>
<source>src/test/scala</source>
</sources>
</configuration>
</execution>
</executions>
</plugin>
<!-- Use scala-maven-plugin for Scala support -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<!-- Need to specify this explicitly, otherwise plugin won't be called when doing e.g. mvn compile -->
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
答案 0 :(得分：1)
在Java 8中添加了它似乎与Java的版本有关。我在集群上有以下版本，但是我如何部署我的代码以及如何避免使用java.time.temporal.TemporalField？：
O(1)。在代码中搜索java.time的任何用法。当前Spark版本需要Java 7和Kafka too，因此它们不应该是问题（但请检查您使用的任何其他库）。
答案 1 :(得分：0)
引起：java.io.NotSerializableException： org.testconsumer.kafka.KafkaEventsConsumer
您的错误跟踪清楚地显示了问题所在。 KafkaProducer未序列化，因此您必须为您的kafka课程@transient添加注释，或者您可以从this获得帮助