为什么在Spark Shell中使用自定义案例类会导致序列化错误？

问题描述 投票：1回答：2

为了我的一生，我不明白为什么这不能序列化。我在下面运行spark-shell（粘贴模式）。我在Spark 1.3.1，Cassandra 2.1.6，Scala 2.10上运行]

import org.apache.spark._
import com.datastax.spark.connector._

val driverPort = 7077
val driverHost = "localhost"
val conf = new SparkConf(true)
  .set("spark.driver.port", driverPort.toString)
  .set("spark.driver.host", driverHost)
  .set("spark.logConf", "true")
  .set("spark.driver.allowMultipleContexts", "true")
  .set("spark.cassandra.connection.host", "localhost")
val sc = new SparkContext("local[*]", "test", conf)
case class Test(id: String, creationdate: String) extends Serializable

sc.parallelize(Seq(Test("98429740-2933-11e5-8e68-f7cca436f8bf", "2015-07-13T07:48:47.924Z")))
  .saveToCassandra("testks", "test", SomeColumns("id", "creationdate"))

sc.cassandraTable[Test]("testks", "test").toArray
sc.stop()

我从此开始了火花壳：

./spark-shell -Ddriver-class-path=/usr/local/spark/libs/* -Dsun.io.serialization.extendedDebugInfo=true

在包含-Dsun.io.serialization.extendedDebugInfo = true属性时没有发现任何区别。

完整错误（已编辑）：

java.lang.reflect.InvocationTargetException
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        at org.apache.spark.serializer.SerializationDebugger$ObjectStreamClassMethods$.getObjFieldValues$extension(SerializationDebugger.scala:240)
        at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visitSerializable(SerializationDebugger.scala:150)
        at org.apache.spark.serializer.SerializationDebugger$SerializationDebugger.visit(SerializationDebugger.scala:99)
        at org.apache.spark.serializer.SerializationDebugger$.find(SerializationDebugger.scala:58)
        at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:39)
        at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:47)
        at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:80)
        at org.apache.spark.scheduler.Task$.serializeWithDependencies(Task.scala:149)
        at org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:464)
        at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$org$apache$spark$scheduler$TaskSchedulerImpl$$resourceOfferSingleTaskSet$1.apply$mcVI$sp(TaskSchedulerImpl.scala:232)
        at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
        at org.apache.spark.scheduler.TaskSchedulerImpl.org$apache$spark$scheduler$TaskSchedulerImpl$$resourceOfferSingleTaskSet(TaskSchedulerImpl.scala:227)
        at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$6.apply(TaskSchedulerImpl.scala:296)
        at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$6.apply(TaskSchedulerImpl.scala:294)
        at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
        at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
        at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:294)
        at org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:294)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
        at org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:294)
        at org.apache.spark.scheduler.local.LocalActor.reviveOffers(LocalBackend.scala:81)
        at org.apache.spark.scheduler.local.LocalActor$$anonfun$receiveWithLogging$1.applyOrElse(LocalBackend.scala:63)
        at scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
        at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
        at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
        at org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:53)
        at org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42)
        at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118)
        at org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42)
        at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
        at org.apache.spark.scheduler.local.LocalActor.aroundReceive(LocalBackend.scala:45)
        at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
        at akka.actor.ActorCell.invoke(ActorCell.scala:487)
        at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
        at akka.dispatch.Mailbox.run(Mailbox.scala:220)
        at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
        at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
Caused by: java.lang.ArrayIndexOutOfBoundsException: 1
        at java.io.ObjectStreamClass$FieldReflector.getObjFieldValues(ObjectStreamClass.java:2050)
        at java.io.ObjectStreamClass.getObjFieldValues(ObjectStreamClass.java:1252)
        ... 45 more
15/08/31 09:04:45 ERROR scheduler.TaskSchedulerImpl: Resource offer failed, task set TaskSet_0 was not serializable

与工作日志不同：

org.apache.spark.SparkContext.runJob(SparkContext.scala:1505)
com.datastax.spark.connector.RDDFunctions.saveToCassandra(RDDFunctions.scala:38)
$line15.$read$$iwC$$iwC.<init>(<console>:30)
$line15.$read$$iwC.<init>(<console>:51)
$line15.$read.<init>(<console>:53)
$line15.$read$.<init>(<console>:57)
$line15.$read$.<clinit>(<console>)
$line15.$eval$.<init>(<console>:7)
$line15.$eval$.<clinit>(<console>)
$line15.$eval.$print(<console>)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$pasteCommand(SparkILoop.scala:824)
scala cassandra apache-spark spark-cassandra-connector
2个回答
4
投票

我非常确定错误的原因是Scala REPL在编译和评估阶段之前将所有表达式包装到一个对象中（请参见Is it reasonable to use Scala's REPL for comparative performance benchmarks?）。在包装表达式时，它会从环境中获取所有对象，在该环境中许多对象可能是无法序列化的val，无法安全地发送给驱动程序（远程进程）。

一种解决方案是在Spark外壳之外定义案例类，并使用--jars两者都包括在CLASSPATH中。

0
投票

将案例类放入单独的mycaseclass.scala文件中，并在Spark Shell中使用：load mycaseclass.scala命令加载该文件也可以。

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