Apache spark 任务在spark中具有不可序列化的结果

我正在尝试使用cassandra驱动程序读取cassandra表。 这是代码

val x = 1 to 2
val rdd = sc.parallelize(x)

val query = "Select data from testkeyspace.testtable where id=%d"

val cc = CassandraConnector(sc.getConf)

val res1 =
    rdd.map{ it => 
            cc.withSessionDo{ session =>
            session.execute( query.format(it))
        }
     }

res1.take(1).foreach(println)

但是我得到的异常任务有一个不可序列化的结果

  org.apache.spark.SparkException: Job aborted due to stage failure: Task 1.0 in stage 24.0 (TID 77) had a not serializable result: com.datastax.driver.core.ArrayBackedResultSet$SinglePage
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1173)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:688)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:688)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1391)
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
    at akka.actor.ActorCell.invoke(ActorCell.scala:456)
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
    at akka.dispatch.Mailbox.run(Mailbox.scala:219)
    at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
    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)

---

如何解决这个问题？

我们转换中的不可序列化对象是从Cassandra返回的结果，这是查询结果上的一个iterable。 您通常希望将该集合具体化到RDD中

一种方法是询问该查询产生的所有记录：

session.execute( query.format(it)).all()

---

即使我做了所有这些，我也会得到同样的例外。“任务有一个不可序列化的结果”@Knight71，原因相同？这次是不同的数据类型

org.apache.spark.scheduler.TaskSetManager:stage 1.0（TID 2）中的Task 1.0有一个不可序列化的结果：com.datastax.driver.core.ArrayBackedRow；不重试。org.apache.spark.sparkeexception:由于阶段失败而中止作业：阶段1.0中的任务1.0（TID 2）有一个不可序列化的结果：com.datastax.driver.core.ArrayBackedRow

@Knight71然后按照breadcrumb:

https://github.com/datastax/java-driver/blob/2.1/driver-core/src/main/java/com/datastax/driver/core/ArrayBackedRow.java

不可序列化。其思想是，

map

操作创建的对象需要可序列化。您需要将该

行

转换为某种可序列化形式：例如

session.execute（query.format（it））.one（）.getXXXXX（“数据”）

（不确定“数据”是什么）谢谢。我试试看。