Github user sun-rui commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r63123410
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala ---
@@ -309,6 +314,82 @@ case class MapGroupsExec(
}
/**
+ * Groups the input rows together and calls the R function with each group
and an iterator
+ * containing all elements in the group.
+ * The result of this function is flattened before being output.
+ */
+case class FlatMapGroupsInRExec(
+ func: Array[Byte],
+ packageNames: Array[Byte],
+ broadcastVars: Array[Broadcast[Object]],
+ inputSchema: StructType,
+ outputSchema: StructType,
+ keyDeserializer: Expression,
+ valueDeserializer: Expression,
+ groupingAttributes: Seq[Attribute],
+ dataAttributes: Seq[Attribute],
+ outputObjAttr: Attribute,
+ child: SparkPlan) extends UnaryExecNode with ObjectOperator {
+
+ override def output: Seq[Attribute] = outputObjAttr :: Nil
+ override def producedAttributes: AttributeSet =
AttributeSet(outputObjAttr)
+
+ override def requiredChildDistribution: Seq[Distribution] =
+ ClusteredDistribution(groupingAttributes) :: Nil
+
+ override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+ Seq(groupingAttributes.map(SortOrder(_, Ascending)))
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ val isSerializedRData =
+ if (inputSchema == SERIALIZED_R_DATA_SCHEMA) true else false
--- End diff --
inputSchmea can't be R serialized schema, because Spark core can't do
shuffle,grouping and sorting on it.
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