ulysses-you commented on code in PR #36698:
URL: https://github.com/apache/spark/pull/36698#discussion_r884620087
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Average.scala:
##########
@@ -75,18 +80,17 @@ abstract class AverageBase
)
protected def getMergeExpressions = Seq(
- /* sum = */ Add(sum.left, sum.right, useAnsiAdd),
+ /* sum = */ add(sum.left, sum.right),
/* count = */ count.left + count.right
)
// If all input are nulls, count will be 0 and we will get null after the
division.
// We can't directly use `/` as it throws an exception under ansi mode.
protected def getEvaluateExpression(queryContext: String) = child.dataType
match {
case _: DecimalType =>
- DecimalPrecision.decimalAndDecimal()(
- Divide(
- CheckOverflowInSum(sum, sumDataType.asInstanceOf[DecimalType],
!useAnsiAdd, queryContext),
- count.cast(DecimalType.LongDecimal), failOnError =
false)).cast(resultType)
+ Divide(
Review Comment:
I think we do not need a `DecimalDivide`, before it was wrapped by
`DecimalPrecision.decimalAndDecimal()` which applied `PromotePrecision` and
`CheckOverflow`. This pr makes `Divide` suuport the semantics of
`PromotePrecision` and `CheckOverflow`, so they are semantically equivalent.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala:
##########
@@ -313,7 +379,7 @@ object BinaryArithmetic {
case class Add(
left: Expression,
right: Expression,
- failOnError: Boolean = SQLConf.get.ansiEnabled) extends BinaryArithmetic {
+ failOnError: Boolean = SQLConf.get.ansiEnabled) extends DecimalArithmetic {
Review Comment:
yeah, better to use a trait
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