EnricoMi commented on code in PR #35899:
URL: https://github.com/apache/spark/pull/35899#discussion_r862699830
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sql/core/src/main/scala/org/apache/spark/sql/KeyValueGroupedDataset.scala:
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@@ -171,6 +171,86 @@ 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.
+ *
+ * @since 3.4.0
+ */
+ def flatMapSortedGroups[S: Encoder, U : Encoder]
Review Comment:
With `V => S` you can pick any columns of `V` you like:
```
case class Value(id, seq, timestamp, value)
val ds: Dataset[Value]
ds.groupBy(v => v.id).flatMapSortedGroups(v => (v.seq, v.timestamp)) { (_,
iter) => iter }
```
The sort order can be added to the function so it can easily be given by the
user:
`(s: V => S, direction: SortDirection = Ascending)`
The `Column`-variant of `flatMapSortedGroups` below also allows for any
number of columns and sort direction.
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