cloud-fan commented on code in PR #37407:
URL: https://github.com/apache/spark/pull/37407#discussion_r977100211
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
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@@ -869,26 +873,55 @@ class Analyzer(override val catalogManager:
CatalogManager)
def apply(plan: LogicalPlan): LogicalPlan =
plan.resolveOperatorsWithPruning(
_.containsPattern(UNPIVOT), ruleId) {
- // once children and ids are resolved, we can determine values, if non
were given
- case up: Unpivot if up.childrenResolved && up.ids.forall(_.resolved) &&
up.values.isEmpty =>
- up.copy(values = up.child.output.diff(up.ids))
+ // once children are resolved, we can determine values from ids and vice
versa
+ // if only either is given
+ case up: Unpivot if up.childrenResolved &&
+ up.ids.exists(_.forall(_.resolved)) && up.values.isEmpty =>
+ up.copy(values =
+ Some(
+ up.child.output.diff(up.ids.get.flatMap(_.references))
Review Comment:
I checked with @HyukjinKwon , even the pandas melt/unpivot API does not
support expressions but only columns. Let's only allow `Attribute` for id and
value columns, for both SQL and Scala API.
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