Github user maryannxue commented on a diff in the pull request:
https://github.com/apache/spark/pull/21926#discussion_r206354004
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -574,10 +578,14 @@ class Analyzer(
// Since evaluating |pivotValues| if statements for each input
row can get slow this is an
// alternate plan that instead uses two steps of aggregation.
val namedAggExps: Seq[NamedExpression] = aggregates.map(a =>
Alias(a, a.sql)())
- val bigGroup = groupByExprs ++ pivotColumn.references
+ val namedPivotCol = pivotColumn match {
--- End diff --
This is to revert the original walk-around aimed to avoid the PivotFirst
issue. Now that we have PivotFirst working alright for complex types, we can
revert it.
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