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From "Thomas Graves (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-10735) CatalystTypeConverters MatchError converting RDD with custom object to dataframe
Date Mon, 21 Sep 2015 19:10:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-10735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Thomas Graves updated SPARK-10735:
----------------------------------
    Priority: Critical  (was: Major)

> CatalystTypeConverters MatchError converting RDD with custom object to dataframe
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-10735
>                 URL: https://issues.apache.org/jira/browse/SPARK-10735
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.5.0
>            Reporter: Thomas Graves
>            Priority: Critical
>
> In spark 1.5.0 we are now seeing an exception when converting an RDD with custom object
to a dataframe.  Note this works with Spark 1.4.1.
> RDD<BasicData>
> where BasicData classhas a field ArrayList<Beacon> where Beacon is a user defined
class now converting RDD<BasicData> to DataFrame is causing the issue:
> 15/09/21 18:53:16 ERROR executor.Executor: Managed memory leak detected; size = 2097152
bytes, TID = 408
> 15/09/21 18:53:16 ERROR executor.Executor: Exception in task 0.0 in stage 4.0 (TID 408)
> scala.MatchError: foo.Beacon@5c289b39 (of class foo.Beacon)
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
 
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:245)
 
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$ArrayConverter.toCatalystImpl(CatalystTypeConverters.scala:164)
  
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$ArrayConverter.toCatalystImpl(CatalystTypeConverters.scala:148)
  
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>         at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:396)
>         at org.apache.spark.sql.SQLContext$$anonfun$9$$anonfun$apply$1$$anonfun$apply$2.apply(SQLContext.scala:494)
>         at org.apache.spark.sql.SQLContext$$anonfun$9$$anonfun$apply$1$$anonfun$apply$2.apply(SQLContext.scala:494)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>         at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>         at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>         at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>         at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>         at org.apache.spark.sql.SQLContext$$anonfun$9$$anonfun$apply$1.apply(SQLContext.scala:494)
>         at org.apache.spark.sql.SQLContext$$anonfun$9$$anonfun$apply$1.apply(SQLContext.scala:492)
>         at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>         at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.processInputs(TungstenAggregationIterator.scala:372)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.start(TungstenAggregationIterator.scala:622)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:110)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>         at org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
>         at org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>         at org.apache.spark.scheduler.Task.run(Task.scala:88)
>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>         at java.lang.Thread.run(Thread.java:745)



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