Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/16063#discussion_r90110048
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/TypeCoercion.scala
---
@@ -482,21 +482,6 @@ object TypeCoercion {
CreateMap(newKeys.zip(newValues).flatMap { case (k, v) => Seq(k,
v) })
- // Promote SUM, SUM DISTINCT and AVERAGE to largest types to prevent
overflows.
- case s @ Sum(e @ DecimalType()) => s // Decimal is already the
biggest.
- case Sum(e @ IntegralType()) if e.dataType != LongType =>
Sum(Cast(e, LongType))
- case Sum(e @ FractionalType()) if e.dataType != DoubleType =>
Sum(Cast(e, DoubleType))
-
- case s @ Average(e @ DecimalType()) => s // Decimal is already the
biggest.
- case Average(e @ IntegralType()) if e.dataType != LongType =>
- Average(Cast(e, LongType))
- case Average(e @ FractionalType()) if e.dataType != DoubleType =>
- Average(Cast(e, DoubleType))
-
- // Hive lets you do aggregation of timestamps... for some reason
- case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType))
- case Average(e @ TimestampType()) => Average(Cast(e, DoubleType))
--- End diff --
Is this safe in terms of backward-compatibility for `Timestamp`?
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