Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/17751#discussion_r113076902
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala ---
@@ -1726,15 +1726,23 @@ class Dataset[T] private[sql](
// It is possible that the underlying dataframe doesn't guarantee the
ordering of rows in its
// constituent partitions each time a split is materialized which
could result in
// overlapping splits. To prevent this, we explicitly sort each input
partition to make the
- // ordering deterministic.
- // MapType cannot be sorted.
- val sorted =
Sort(logicalPlan.output.filterNot(_.dataType.isInstanceOf[MapType])
- .map(SortOrder(_, Ascending)), global = false, logicalPlan)
+ // ordering deterministic. Note that MapTypes cannot be sorted and are
explicitly pruned out
+ // from the sort order.
+ val sortOrder = logicalPlan.output
+ .filterNot(_.dataType.existsRecursively(dt =>
dt.isInstanceOf[MapType]))
--- End diff --
How about calling `RowOrdering.isOrderable`?
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