Github user chenghao-intel commented on a diff in the pull request:
https://github.com/apache/spark/pull/7202#discussion_r33839886
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
---
@@ -28,7 +28,45 @@ import org.apache.spark.util.Utils
* A non-concrete data type, reserved for internal uses.
*/
private[sql] abstract class AbstractDataType {
+ /**
+ * The default concrete type to use if we want to cast a null literal
into this type.
+ */
private[sql] def defaultConcreteType: DataType
+
+ /**
+ * Returns true if this data type is a parent of the `childCandidate`.
+ */
+ private[sql] def isParentOf(childCandidate: DataType): Boolean
+}
+
+
+/**
+ * A collection of types that can be used to specify type constraints. The
sequence also specifies
+ * precedence: an earlier type takes precedence over a latter type.
+ *
+ * {{{
+ * TypeCollection(StringType, BinaryType)
+ * }}}
+ *
+ * This means that we prefer StringType over BinaryType if it is possible
to cast to StringType.
+ */
+private[sql] class TypeCollection(private val types: Seq[DataType])
extends AbstractDataType {
+ require(types.nonEmpty, s"TypeCollection ($types) cannot be empty")
+
+ private[sql] override def defaultConcreteType: DataType = types.head
--- End diff --
This is probably not always true for the `Expression`, the
`defaultConcreteType` depends on what the data type of child expression in lots
of case.
e.g. `Length` expects both `StringType` and `BinaryType`, `Hex` etc.
And we probably need to review those expressions override the function of
`checkInputDataTypes()`.
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