[GitHub] [spark] zhengruifeng commented on a diff in pull request #39017: [SPARK-41440][CONNECT][PYTHON] Implement `DataFrame.randomSplit`

[GitBox <gi...@apache.org>]

zhengruifeng commented on code in PR #39017:
URL: https://github.com/apache/spark/pull/39017#discussion_r1047994969


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connector/connect/server/src/main/scala/org/apache/spark/sql/connect/planner/SparkConnectPlanner.scala:
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@@ -132,12 +132,31 @@ class SparkConnectPlanner(session: SparkSession) {
    * wrap such fields into proto messages.
    */
   private def transformSample(rel: proto.Sample): LogicalPlan = {
+    val logicalPlan = transformRelation(rel.getInput)
+    val plan = if (rel.getForceStableSort) {
+      // 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. Note that MapTypes cannot be sorted and are explicitly pruned out
+      // from the sort order.
+      val sortOrder = logicalPlan.output
+        .filter(attr => RowOrdering.isOrderable(attr.dataType))
+        .map(SortOrder(_, Ascending))
+      if (sortOrder.nonEmpty) {
+        Sort(sortOrder, global = false, logicalPlan)
+      } else {
+        logicalPlan

Review Comment:
   `Dataset.randomSplit` will do caching. https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala#L2570-L2572



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