Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/999#discussion_r13828272
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
---
@@ -108,19 +118,18 @@ trait HiveTypeCoercion {
*
* Additionally, all types when UNION-ed with strings will be promoted
to strings.
* Other string conversions are handled by PromoteStrings.
+ *
+ * Widening types might result in loss of precision in the following
cases:
+ * - IntegerType to FloatType
+ * - LongType to FloatType
+ * - LongType to DoubleType
*/
object WidenTypes extends Rule[LogicalPlan] {
- // See
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types.
- // The conversion for integral and floating point types have a linear
widening hierarchy:
- val numericPrecedence =
- Seq(NullType, ByteType, ShortType, IntegerType, LongType, FloatType,
DoubleType, DecimalType)
- // Boolean is only wider than Void
- val booleanPrecedence = Seq(NullType, BooleanType)
- val allPromotions: Seq[Seq[DataType]] = numericPrecedence ::
booleanPrecedence :: Nil
def findTightestCommonType(t1: DataType, t2: DataType):
Option[DataType] = {
// Try and find a promotion rule that contains both types in
question.
- val applicableConversion = allPromotions.find(p => p.contains(t1) &&
p.contains(t2))
+ val applicableConversion = HiveTypeCoercion.allPromotions.find(p =>
p.contains(t1) && p
--- End diff --
In general I'd prefer to break at a higher syntactic level. (i.e., right
after the && or even put the whole command on the next line with only the `val
applicableConversion =` on the line above).
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