Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r66712035
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/RelationalGroupedDataset.scala ---
@@ -381,6 +385,50 @@ class RelationalGroupedDataset protected[sql](
def pivot(pivotColumn: String, values: java.util.List[Any]):
RelationalGroupedDataset = {
pivot(pivotColumn, values.asScala)
}
+
+ /**
+ * Applies the given serialized R function `func` to each group of data.
For each unique group,
+ * the function will be passed the group key and an iterator that
contains all of the elements in
+ * the group. The function can return an iterator containing elements of
an arbitrary type which
+ * will be returned as a new [[DataFrame]].
+ *
+ * This function does not support partial aggregation, and as a result
requires shuffling all
+ * the data in the [[Dataset]]. If an application intends to perform an
aggregation over each
+ * key, it is best to use the reduce function or an
+ * [[org.apache.spark.sql.expressions#Aggregator Aggregator]].
+ *
+ * Internally, the implementation will spill to disk if any given group
is too large to fit into
+ * memory. However, users must take care to avoid materializing the
whole iterator for a group
+ * (for example, by calling `toList`) unless they are sure that this is
possible given the memory
+ * constraints of their cluster.
+ *
+ * @since 2.0.0
+ */
+ private[sql] def flatMapGroupsInR(
+ f: Array[Byte],
+ packageNames: Array[Byte],
+ broadcastVars: Array[Object],
+ outputSchema: StructType): DataFrame = {
+ val broadcastVarObj =
broadcastVars.map(_.asInstanceOf[Broadcast[Object]])
+ val groupingNamedExpressions = groupingExprs.map(alias)
+ val groupingCols = groupingNamedExpressions.map(Column(_))
+ val groupingDataFrame = df.select(groupingCols : _*)
+ val groupingAttributes = groupingNamedExpressions.map(_.toAttribute)
+ val realOutputSchema = if (outputSchema == null)
SERIALIZED_R_DATA_SCHEMA else outputSchema
--- End diff --
@liancheng , thank you for the review comments.
Those are good suggestions, however for:
case 1: using Option[StructType] ... - I gave a try but since this
method is being called from R side we need to somehow instantiate
"scala.Option" class and this doesn't seem to be primitive to do in R.
From R side we will basically call the following method:
`org.apache.spark.sql.Dataset<org.apache.spark.sql.Row> flatMapGroupsInR
(byte[] f, byte[] packageNames, java.lang.Object[] broadcastVars,
scala.Option<org.apache.spark.sql.types.StructType> outputSchema)`
Case 2: Similar to dapply, gapply forces schema by signature, the default
value doesn't really work here.
But I can make the changes if it is preferred.
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