Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/23143#discussion_r236267692
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
---
@@ -251,19 +251,15 @@ case class ExpressionEncoder[T](
*/
def isSerializedAsStruct: Boolean =
objSerializer.dataType.isInstanceOf[StructType]
- /**
- * Returns true if the type `T` is an `Option` type.
- */
- def isOptionType: Boolean =
classOf[Option[_]].isAssignableFrom(clsTag.runtimeClass)
-
/**
* If the type `T` is serialized as a struct, when it is encoded to a
Spark SQL row, fields in
* the struct are naturally mapped to top-level columns in a row. In
other words, the serialized
* struct is flattened to row. But in case of the `T` is also an
`Option` type, it can't be
* flattened to top-level row, because in Spark SQL top-level row can't
be null. This method
* returns true if `T` is serialized as struct and is not `Option` type.
*/
- def isSerializedAsStructForTopLevel: Boolean = isSerializedAsStruct &&
!isOptionType
+ def isSerializedAsStructForTopLevel: Boolean = isSerializedAsStruct &&
--- End diff --
nit: since it cross lines, I'd prefer
```
def xxx = {
xxx
}
```
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