sunchao commented on a change in pull request #29792:
URL: https://github.com/apache/spark/pull/29792#discussion_r496924894
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/UnwrapCastInBinaryComparison.scala
##########
@@ -200,25 +252,27 @@ object UnwrapCastInBinaryComparison extends
Rule[LogicalPlan] {
/**
* Check if the input `fromExp` can be safely cast to `toType` without any
loss of precision,
* i.e., the conversion is injective. Note this only handles the case when
both sides are of
- * integral type.
+ * numeric type.
*/
private def canImplicitlyCast(
fromExp: Expression,
toType: DataType,
literalType: DataType): Boolean = {
toType.sameType(literalType) &&
!fromExp.foldable &&
- fromExp.dataType.isInstanceOf[IntegralType] &&
- toType.isInstanceOf[IntegralType] &&
+ fromExp.dataType.isInstanceOf[NumericType] &&
+ toType.isInstanceOf[NumericType] &&
Cast.canUpCast(fromExp.dataType, toType)
}
- private def getRange(dt: DataType): (Any, Any) = dt match {
- case ByteType => (Byte.MinValue, Byte.MaxValue)
- case ShortType => (Short.MinValue, Short.MaxValue)
- case IntegerType => (Int.MinValue, Int.MaxValue)
- case LongType => (Long.MinValue, Long.MaxValue)
- case other => throw new IllegalArgumentException(s"Unsupported type:
${other.catalogString}")
+ private def getRange(dt: DataType): Option[(Any, Any)] = dt match {
+ case ByteType => Some((Byte.MinValue, Byte.MaxValue))
+ case ShortType => Some((Short.MinValue, Short.MaxValue))
+ case IntegerType => Some((Int.MinValue, Int.MaxValue))
+ case LongType => Some((Long.MinValue, Long.MaxValue))
+ case FloatType => Some((Float.NegativeInfinity, Float.NaN))
Review comment:
This is because `PositiveInfinity` is considered to be < `NaN` in Spark.
If we treat it as the upper bound, rules handling the upper bounds will not be
valid. For instance the following expr:
```sql
cast(e as double) > double('+inf')
```
would be converted to
```sql
e === double('+inf')
```
which won't be correct if `e` evaluates to `double('NaN')`.
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