Chao Gao created SPARK-39352:
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Summary: There are problems in canUpCast function
Key: SPARK-39352
URL: https://issues.apache.org/jira/browse/SPARK-39352
Project: Spark
Issue Type: Bug
Components: Spark Core
Affects Versions: 3.2.1
Reporter: Chao Gao
Spark's canUpCast means one value can be cast to another type without the loss
of precision. But actually long type can not be cast to float/double
{code:java}
def canUpCast(from: DataType, to: DataType): Boolean = (from, to) match {
case _ if from == to => true
case (from: NumericType, to: DecimalType) if to.isWiderThan(from) => true
case (from: DecimalType, to: NumericType) if from.isTighterThan(to) => true
case (f, t) if legalNumericPrecedence(f, t) => true
case (DateType, TimestampType) => true
case (_: AtomicType, StringType) => true
case (_: CalendarIntervalType, StringType) => true
case (NullType, _) => true
... {code}
{code:java}
private def legalNumericPrecedence(from: DataType, to: DataType): Boolean = {
val fromPrecedence = TypeCoercion.numericPrecedence.indexOf(from)
val toPrecedence = TypeCoercion.numericPrecedence.indexOf(to) fromPrecedence >=
0 && fromPrecedence < toPrecedence
}
{code}
{code:java}
val numericPrecedence =
IndexedSeq(
ByteType,
ShortType,
IntegerType,
LongType,
FloatType,
DoubleType) {code}
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