Hi,
I am processing multiple 2 GB each csv files with my spark application. Which
also does union and aggregation across all the input files. Currently stuck
with given below error:
java.lang.StackOverflowError
at
org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression.dataType(interfaces.scala:81)
at
org.apache.spark.sql.catalyst.expressions.Alias.toAttribute(namedExpressions.scala:149)
at
org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$7.apply(basicOperators.scala:228)
at
org.apache.spark.sql.catalyst.plans.logical.Aggregate$$anonfun$output$7.apply(basicOperators.scala:228)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at
org.apache.spark.sql.catalyst.plans.logical.Aggregate.output(basicOperators.scala:228)
at
org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:38)
at
org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:38)
at scala.Option.getOrElse(Option.scala:120)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at
org.apache.spark.sql.catalyst.plans.logical.Aggregate.output(basicOperators.scala:228)
at
org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:38)
at
org.apache.spark.sql.catalyst.planning.PhysicalOperation$$anonfun$unapply$1.apply(patterns.scala:38)
at scala.Option.getOrElse(Option.scala:120)
at
org.apache.spark.sql.catalyst.planning.PhysicalOperation$.unapply(patterns.scala:38)
at
org.apache.spark.sql.execution.datasources.DataSourceStrategy$.apply(DataSourceStrategy.scala:44)
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:334)
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$$anon$13.hasNext(Iterator.scala:371)
at
org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59)
at
org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54)
at
org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:327)
at
org.apache.spark.sql.catalyst.planning.QueryPlanner$$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)
I understand that it could be due to RDD linage and tried to resolve it via
checkpoint, but no luck.
Any help?
Sincerely,
-Vivek
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