cloud-fan commented on code in PR #39640:
URL: https://github.com/apache/spark/pull/39640#discussion_r1080733239
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sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
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@@ -171,6 +171,84 @@ class KeyValueGroupedDataset[K, V] private[sql](
flatMapGroups((key, data) => f.call(key, data.asJava).asScala)(encoder)
}
+ /**
+ * (Scala-specific)
+ * Applies the given function to each group of data. For each unique group,
the function will
+ * be passed the group key and a sorted 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 [[Dataset]].
+ *
+ * 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`.
+ *
+ * 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.
+ *
+ * This is equivalent to [[KeyValueGroupedDataset#flatMapGroups]], except
for the iterator
+ * to be sorted according to the given sort expressions. That sorting does
not add
+ * computational complexity.
+ *
+ * @see [[org.apache.spark.sql.KeyValueGroupedDataset#flatMapGroups]]
+ * @since 3.4.0
+ */
+ def flatMapSortedGroups[U : Encoder]
+ (sortExprs: Column*)
+ (f: (K, Iterator[V]) => TraversableOnce[U]): Dataset[U] = {
+ val sortOrder: Seq[SortOrder] = sortExprs.map { col =>
+ col.expr match {
+ case expr: SortOrder => expr
+ case expr: Expression => SortOrder(expr, Ascending)
+ }
+ }
+
+ Dataset[U](
+ sparkSession,
+ MapGroups(
+ f,
+ groupingAttributes,
+ dataAttributes,
+ sortOrder,
+ logicalPlan
+ )
+ )
+ }
+
+
+ /**
+ * (Java-specific)
Review Comment:
BTW `Column*` is both scala and java friendly, we don't need to change it to
array.
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