ulysses-you commented on code in PR #38739:
URL: https://github.com/apache/spark/pull/38739#discussion_r1031054191
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sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala:
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@@ -276,9 +276,9 @@ class DecimalPrecisionSuite extends AnalysisTest with
BeforeAndAfter {
val a = AttributeReference("a", DecimalType(3, -10))()
val b = AttributeReference("b", DecimalType(1, -1))()
val c = AttributeReference("c", DecimalType(35, 1))()
- checkType(Multiply(a, b), DecimalType(5, -11))
- checkType(Multiply(a, c), DecimalType(38, -9))
- checkType(Multiply(b, c), DecimalType(37, 0))
+ checkType(Multiply(a, b), DecimalType(16, 0))
Review Comment:
Not sure what you found. I think the reason why Divide and IntegralDivide
fail is simple. SQL strandard does not allow negative scale, but we use its
definition formula to calculate the result precision and scale. Then the result
precision can be nagetive which is unexpected. So I think other binary
arithmetic also should not follow if scale is negative.
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