kazuyukitanimura commented on code in PR #43853:
URL: https://github.com/apache/spark/pull/43853#discussion_r1396777531
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sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala:
##########
@@ -308,25 +308,38 @@ class ArithmeticExpressionSuite extends SparkFunSuite
with ExpressionEvalHelper
val mulResult = Decimal(mulExact.setScale(mulType.scale,
RoundingMode.HALF_UP))
val mulExpected =
if (mulResult.precision > DecimalType.MAX_PRECISION) null else
mulResult
- checkEvaluation(mulActual, mulExpected)
+ tryCheckEvaluation(mulActual, mulExpected)
val divType = Divide(null, null).resultDecimalType(p1, s1, p2, s2)
val divResult = Decimal(divExact.setScale(divType.scale,
RoundingMode.HALF_UP))
val divExpected =
if (divResult.precision > DecimalType.MAX_PRECISION) null else
divResult
- checkEvaluation(divActual, divExpected)
+ tryCheckEvaluation(divActual, divExpected)
val remType = Remainder(null, null).resultDecimalType(p1, s1, p2, s2)
val remResult = Decimal(remExact.setScale(remType.scale,
RoundingMode.HALF_UP))
val remExpected =
if (remResult.precision > DecimalType.MAX_PRECISION) null else
remResult
- checkEvaluation(remActual, remExpected)
+ tryCheckEvaluation(remActual, remExpected)
val quotType = IntegralDivide(null, null).resultDecimalType(p1, s1,
p2, s2)
val quotResult = Decimal(quotExact.setScale(quotType.scale,
RoundingMode.HALF_UP))
val quotExpected =
if (quotResult.precision > DecimalType.MAX_PRECISION) null else
quotResult
- checkEvaluation(quotActual, quotExpected.toLong)
+ tryCheckEvaluation(quotActual, quotExpected.toLong)
+ }
+ }
+
+ def tryCheckEvaluation(actual: BinaryArithmetic, expected: Any): Unit = {
+ try {
+ checkEvaluation(actual, expected)
+ }
+ catch {
+ // Ignore NUMERIC_VALUE_OUT_OF_RANGE when ANSI is enabled
+ case e: org.scalatest.exceptions.TestFailedException
+ if e.cause.exists(c => c.isInstanceOf[SparkArithmeticException] &&
+ c.asInstanceOf[SparkArithmeticException].getErrorClass
+ == "NUMERIC_VALUE_OUT_OF_RANGE") && SQLConf.get.ansiEnabled =>
Review Comment:
Thank you @dongjoon-hyun @LuciferYang
No behavior change introduced by #43678
This test was assuming the default spark.sql.ansi.enabled=false. The default
behavior does not throw the exception for overflows, but Ansi mode does. Since
this is a random value test, we may have combinations that overflows.
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