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Posted to issues@spark.apache.org by "Max Schmidt (JIRA)" <ji...@apache.org> on 2015/08/25 12:44:45 UTC

[jira] [Created] (SPARK-10221) RowReaderFactory does not work with blobs

Max Schmidt created SPARK-10221:
-----------------------------------

             Summary: RowReaderFactory does not work with blobs
                 Key: SPARK-10221
                 URL: https://issues.apache.org/jira/browse/SPARK-10221
             Project: Spark
          Issue Type: Bug
            Reporter: Max Schmidt


While using a RowReaderFactory out of the Util API here: com.datastax.spark.connector.japi.CassandraJavaUtil.mapRowToTuple(, Class<ByteBuffer>) against a cassandra table with a column which is described as a ByteBuffer get the following stacktrace:

8786 [task-result-getter-0] ERROR org.apache.spark.scheduler.TaskSetManager  - Task 0.0 in stage 0.0 (TID 0) had a not serializable result: java.nio.HeapByteBuffer
Serialization stack:
        - object not serializable (class: java.nio.HeapByteBuffer, value: java.nio.HeapByteBuffer[pos=0 lim=2 cap=2])
        - field (class: scala.Tuple4, name: _2, type: class java.lang.Object)
        - object (class scala.Tuple4, (/104.130.160.121,java.nio.HeapByteBuffer[pos=0 lim=2 cap=2],Tue Aug 25 11:00:23 CEST 2015,76.808)); not retrying
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: java.nio.HeapByteBuffer
Serialization stack:
        - object not serializable (class: java.nio.HeapByteBuffer, value: java.nio.HeapByteBuffer[pos=0 lim=2 cap=2])
        - field (class: scala.Tuple4, name: _2, type: class java.lang.Object)
        - object (class scala.Tuple4, (/104.130.160.121,java.nio.HeapByteBuffer[pos=0 lim=2 cap=2],Tue Aug 25 11:00:23 CEST 2015,76.808))
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1273)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1264)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1263)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1263)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
        at scala.Option.foreach(Option.scala:236)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1457)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1418)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)

Using a kind of wrapper-class following bean conventions, doesn't work either.



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