I deployed a Spark application and encounter this error:
org.apache.spark.SparkException: Job aborted due to stage failure: Failed to serialize task 602, not attempting to retry it. Exception during serialization: java.io.NotSerializableException: scala.Unit$
Serialization stack:
- object not serializable (class: scala.Unit$, value: object scala.Unit)
- element of array (index: 0)
- array (class [Lscala.Unit$;, size 1)
- field (class: scala.collection.mutable.WrappedArray$ofRef, name: array, type: class [Ljava.lang.Object;)
- object (class scala.collection.mutable.WrappedArray$ofRef, WrappedArray(object scala.Unit))
- writeObject data (class: org.apache.spark.rdd.ParallelCollectionPartition)
- object (class org.apache.spark.rdd.ParallelCollectionPartition, org.apache.spark.rdd.ParallelCollectionPartition@2699)
- field (class: org.apache.spark.scheduler.ShuffleMapTask, name: partition, type: interface org.apache.spark.Partition)
- object (class org.apache.spark.scheduler.ShuffleMapTask, ShuffleMapTask(68, 0))
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1889)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1876)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:274)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD.$anonfun$foreach$1(RDD.scala:927)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
at org.apache.spark.rdd.RDD.foreach(RDD.scala:925)
This error is extremely rare. And cannot be found anywhere on the internet. I'm under the impression that it is impossible for this error to happen, as Unit type & singleton will be removed by Scala compiler and become void in JVM bytecode.
Why this problem could happen and how do I eliminiate it in the future?
Sorry forgot to describe the environment:
The Spark application was compiled for Spark-2.4 & scala-2.12, and deployed in local-[*] mode