XiDuo You created SPARK-39316:
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Summary: Merge PromotePrecision and CheckOverflow into decimal
binary arithmetic
Key: SPARK-39316
URL: https://issues.apache.org/jira/browse/SPARK-39316
Project: Spark
Issue Type: Sub-task
Components: SQL
Affects Versions: 3.4.0
Reporter: XiDuo You
Merge `PromotePrecision` into `dataType`, so every arithmetic should report the
accurate decimal type.
For example, `Add`:
{code:java}
override def dataType: DataType = (left, right) match {
case (DecimalType.Expression(p1, s1), DecimalType.Expression(p2, s2)) =>
val resultScale = max(s1, s2)
if (allowPrecisionLoss) {
DecimalType.adjustPrecisionScale(max(p1 - s1, p2 - s2) + resultScale + 1,
resultScale)
} else {
DecimalType.bounded(max(p1 - s1, p2 - s2) + resultScale + 1, resultScale)
}
case _ => super.dataType
} {code}
Merge `CheckOverflow` into eval and code-gen code path, so every arithmetic can
handle the overflow case during runtime.
For example, `Add`:
{code:java}
dataType match {
case decimalType: DecimalType =>
val value = numeric.plus(input1, input2)
checkOverflow(value.asInstanceOf[Decimal], decimalType)
...
} {code}
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