AngersZhuuuu commented on a change in pull request #27861:
URL: https://github.com/apache/spark/pull/27861#discussion_r413477419
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/parser/AstBuilder.scala
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@@ -1691,7 +1691,19 @@ class AstBuilder(conf: SQLConf) extends
SqlBaseBaseVisitor[AnyRef] with Logging
override def visitWindowDef(ctx: WindowDefContext): WindowSpecDefinition =
withOrigin(ctx) {
// CLUSTER BY ... | PARTITION BY ... ORDER BY ...
val partition = ctx.partition.asScala.map(expression)
- val order = ctx.sortItem.asScala.map(visitSortItem)
+ val order = if (ctx.sortItem.asScala.nonEmpty) {
+ ctx.sortItem.asScala.map(visitSortItem)
+ } else if (ctx.windowFrame != null &&
+ ctx.windowFrame().frameType.getType == SqlBaseParser.RANGE) {
+ // for RANGE window frame, we won't add default order spec
+ ctx.sortItem.asScala.map(visitSortItem)
+ } else {
+ // Same default behaviors like hive, when order spec is null
+ // set partition spec expression as order spec
+ ctx.partition.asScala.map { expr =>
+ SortOrder(expression(expr), Ascending, Ascending.defaultNullOrdering,
Set.empty)
Review comment:
> deterministic
For same sql, result is deterministic.
And we add partition column as order by column by default can keep result
deterministic.
I meet this problem when migration hive sql to spark sql.
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