Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r66745283
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala ---
@@ -325,6 +330,71 @@ 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 ObjectProducerExec {
+
+ 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 (outputSchema == SERIALIZED_R_DATA_SCHEMA) true else false
+ val serializerForR = if (!isSerializedRData) {
+ SerializationFormats.ROW
+ } else {
+ SerializationFormats.BYTE
+ }
+
+ child.execute().mapPartitionsInternal { iter =>
+ val grouped = GroupedIterator(iter, groupingAttributes, child.output)
+ val getKey = ObjectOperator.deserializeRowToObject(keyDeserializer,
groupingAttributes)
+ val getValue =
ObjectOperator.deserializeRowToObject(valueDeserializer, dataAttributes)
+ val outputObject =
ObjectOperator.wrapObjectToRow(outputObjAttr.dataType)
+ val runner = new RRunner[Array[Byte]](
+ func, SerializationFormats.ROW, serializerForR, packageNames,
broadcastVars,
+ isDataFrame = true, colNames = inputSchema.fieldNames, mode = 2)
+
+ val groupedRBytes = grouped.flatMap { case (key, rowIter) =>
--- End diff --
Hi @sun-rui, I did it similar to:
https://github.com/NarineK/spark/blob/d51441f704e2abad7f7a3cc829664cd201b0fcd2/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala#L322
I we can also use map here. If there is no nested content flatmap will
perform like map. In general for dataframe rows, can someone have a row of rows
?
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