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https://issues.apache.org/jira/browse/TOREE-428?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16240452#comment-16240452
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Paul Balm commented on TOREE-428:
---------------------------------
I confirm this issue. This is my test case (slightly simpler):
* Create the test data from the terminal: {{F=arraystore.csv ; echo a > $F;
echo b >> $F; echo c >> $F}}
* Read the file into an RDD with a case class:
{noformat}
case class IdClass(id: String)
sc.textFile("arraystore.csv").map(IdClass).collect()
{noformat}
This produces the stacktrace in the description.
I don't have any particular insights into the problem, but an
{{ArrayStoreException}} is normally an indication that an object is being
stored in an array with an incompatible type. For example when you have an
array Strings and you're trying to put an {{IdClass}} or {{Person}} object into
it.
> Can't use case class in the Scala notebook
> ------------------------------------------
>
> Key: TOREE-428
> URL: https://issues.apache.org/jira/browse/TOREE-428
> Project: TOREE
> Issue Type: Bug
> Components: Build
> Reporter: Haifeng Li
>
> the version of docker:
> jupyter/all-spark-notebook:lastest
> the way to start docker:
> docker run -it --rm -p 8888:8888 jupyter/all-spark-notebook:latest
> or
> docker ps -a
> docker start -i containerID
> the steps:
> Visit http://localhost:8888
> Start an toree notebook
> input code above
> {code:java}
> import spark.implicits._
> val p = spark.sparkContext.textFile ("../Data/person.txt")
> val pmap = p.map ( _.split (","))
> pmap.collect()
> {code}
> the output:res0: Array[Array[String]] = Array(Array(Barack, Obama, 53),
> Array(George, Bush, 68), Array(Bill, Clinton, 68))
> {code:java}
> case class Persons (first_name:String,last_name: String,age:Int)
> val personRDD = pmap.map ( p => Persons (p(0), p(1), p(2).toInt))
> personRDD.take(1)
> {code}
> the error message:
> {code:java}
> org.apache.spark.SparkDriverExecutionException: Execution error
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1186)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1711)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062)
> at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1354)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.take(RDD.scala:1327)
> ... 39 elided
> Caused by: java.lang.ArrayStoreException: [LPersons;
> at scala.runtime.ScalaRunTime$.array_update(ScalaRunTime.scala:90)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:2043)
> at
> org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:2043)
> at org.apache.spark.scheduler.JobWaiter.taskSucceeded(JobWaiter.scala:59)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1182)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1711)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> {code}
> The above code is working with the spark-shell. From error message, I
> speculated that the driver program didn't correctly handle case class Persons
> to RDD partition.
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