Github user adrian-wang commented on a diff in the pull request:
https://github.com/apache/spark/pull/2246#discussion_r17092407
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala
---
@@ -125,12 +125,16 @@ private[sql] object JsonRDD extends Logging {
* Returns the most general data type for two given data types.
*/
private[json] def compatibleType(t1: DataType, t2: DataType): DataType =
{
- // Try and find a promotion rule that contains both types in question.
- val applicableConversion = HiveTypeCoercion.allPromotions.find(p =>
p.contains(t1) && p
- .contains(t2))
+ val returnType = if (t1 == t2) {
+ Some(t1)
+ } else {
+ // Try and find a promotion rule that contains both types in
question.
+ val applicableConversion = HiveTypeCoercion.allPromotions.find(p =>
p.contains(t1) && p
+ .contains(t2))
- // If found return the widest common type, otherwise None
- val returnType = applicableConversion.map(_.filter(t => t == t1 || t
== t2).last)
+ // If found return the widest common type, otherwise None
+ applicableConversion.map(_.filter(t => t == t1 || t == t2).last)
--- End diff --
But I think in the future we should also support complex type promotion in
`findTightestCommonType(t1, t2)`. Anyway we can extract the current
`findTightestCommonType(t1, t2)` as a util function. What do you prefer?
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