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https://issues.apache.org/jira/browse/SPARK-13795?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-13795:
---------------------------------
Labels: bulk-closed (was: )
> ClassCast Exception while attempting to show() a DataFrame
> ----------------------------------------------------------
>
> Key: SPARK-13795
> URL: https://issues.apache.org/jira/browse/SPARK-13795
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.0
> Environment: Linux 14.04 LTS
> Reporter: Ganesh Krishnan
> Priority: Major
> Labels: bulk-closed
>
> DataFrame Schema (by printSchema() ) is as follows
> allDataJoined.printSchema()
> {noformat}
> |-- eventType: string (nullable = true)
> |-- itemId: string (nullable = true)
> |-- productId: string (nullable = true)
> |-- productVersion: string (nullable = true)
> |-- servicedBy: string (nullable = true)
> |-- ACCOUNT_NAME: string (nullable = true)
> |-- CONTENTGROUPID: string (nullable = true)
> |-- PRODUCT_ID: string (nullable = true)
> |-- PROFILE_ID: string (nullable = true)
> |-- SALESADVISEREMAIL: string (nullable = true)
> |-- businessName: string (nullable = true)
> |-- contentGroupId: string (nullable = true)
> |-- salesAdviserName: string (nullable = true)
> |-- salesAdviserPhone: string (nullable = true)
> {noformat}
> There is NO column that has any datatype except String. There used to be
> previously an inferred column of type long that was dropped
>
> {code}
> DataFrame allDataJoined = whiteEventJoinedWithReference.
> drop(rliDataFrame.col("occurredAtDate"));
> allDataJoined.printSchema() : output above ^^
> Now
> allDataJoined.show()
>
> {code}
> throws the following exception vv
> {noformat}
> java.lang.ClassCastException: java.lang.Long cannot be cast to
> java.lang.Integer
> at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
> at scala.math.Ordering$Int$.compare(Ordering.scala:256)
> at scala.math.Ordering$class.gt(Ordering.scala:97)
> at scala.math.Ordering$Int$.gt(Ordering.scala:256)
> at
> org.apache.spark.sql.catalyst.expressions.GreaterThan.nullSafeEval(predicates.scala:457)
> at
> org.apache.spark.sql.catalyst.expressions.BinaryExpression.eval(Expression.scala:383)
> at
> org.apache.spark.sql.catalyst.expressions.And.eval(predicates.scala:238)
> at
> org.apache.spark.sql.catalyst.expressions.InterpretedPredicate$$anonfun$create$2.apply(predicates.scala:38)
> at
> org.apache.spark.sql.catalyst.expressions.InterpretedPredicate$$anonfun$create$2.apply(predicates.scala:38)
> at
> org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$prunePartitions$1.apply(DataSourceStrategy.scala:257)
> at
> org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$prunePartitions$1.apply(DataSourceStrategy.scala:257)
> at
> scala.collection.TraversableLike$$anonfun$filter$1.apply(TraversableLike.scala:264)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> scala.collection.TraversableLike$class.filter(TraversableLike.scala:263)
> at scala.collection.AbstractTraversable.filter(Traversable.scala:105)
> at
> org.apache.spark.sql.execution.datasources.DataSourceStrategy$.prunePartitions(DataSourceStrategy.scala:257)
> at
> org.apache.spark.sql.execution.datasources.DataSourceStrategy$.apply(DataSourceStrategy.scala:82)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
> at
> org.apache.spark.sql.execution.SparkStrategies$EquiJoinSelection$.makeBroadcastHashJoin(SparkStrategies.scala:88)
> at
> org.apache.spark.sql.execution.SparkStrategies$EquiJoinSelection$.apply(SparkStrategies.scala:97)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
> at
> org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:336)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
> at
> org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:349)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58)
> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
> at
> org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:47)
> at
> org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:45)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:52)
> at
> org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:52)
> at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2134)
> at org.apache.spark.sql.DataFrame.head(DataFrame.scala:1413)
> at org.apache.spark.sql.DataFrame.take(DataFrame.scala:1495)
> at org.apache.spark.sql.DataFrame.showString(DataFrame.scala:171)
> at org.apache.spark.sql.DataFrame.show(DataFrame.scala:394)
> at org.apache.spark.sql.DataFrame.show(DataFrame.scala:355)
> at org.apache.spark.sql.DataFrame.show(DataFrame.scala:363)
> {noformat}
> Checked, googled, stackoverflowed with no results.
> Edit: I managed to narrow down this bug to this usecase scenario:
> The raw json has the field dateOccuredAt and also the parquet it is being
> written to also has partition dateOccuredAt. The raw JSON field is being
> inferred as String while the partition is inferred as long which is correct
> too. However while persisting we have the above error even if the column
> dateOccuredAt is dropped from the DataFrame
> Also, we use Java and not Scala.
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