EnricoMi commented on code in PR #37407:
URL: https://github.com/apache/spark/pull/37407#discussion_r982517306
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sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
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@@ -869,26 +869,50 @@ 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))
-
- case up: Unpivot if !up.childrenResolved || !up.ids.forall(_.resolved) ||
- up.values.isEmpty || !up.values.forall(_.resolved) ||
!up.valuesTypeCoercioned => up
+ // 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 =>
+ val idAttrs = AttributeSet(up.ids.get)
Review Comment:
I am confused. You are saying it would be OK for PySpark `unpivot` to take
expressions as long as both, `ids` and `values` are given, even though `pivot`
and `pandas.melt` take only column names?
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