Github user sun-rui commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r62602833
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala ---
@@ -113,6 +116,46 @@ class KeyValueGroupedDataset[K, V] private[sql](
}
/**
+ * Applies the given R function 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 [[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 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
+ */
+ def flatMapRGroups(
--- End diff --
Sorry for previous suggestion that add new method in
KeyValueGroupedDataset. After thoughts, I think this method should be added in
RelationalGroupedDataset, as we are support gapply() for DataFrame, which is
column-oriented. In the future, if we support Dataset API, we can add a similar
new method in KeyValueGroupedDataset.
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