cloud-fan commented on code in PR #36150:
URL: https://github.com/apache/spark/pull/36150#discussion_r923029449
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:
##########
@@ -1245,6 +1245,44 @@ case class Pivot(
override protected def withNewChildInternal(newChild: LogicalPlan): Pivot =
copy(child = newChild)
}
+/**
+ * A constructor for creating an Unpivot, which will later be converted to an
[[Expand]]
+ * during the query analysis.
+ *
+ * An empty values array will be replaced during analysis with all resolved
outputs of child except
+ * the ids. This expansion allows to easily unpivot all non-id columns.
+ *
+ * @see `org.apache.spark.sql.catalyst.analysis.Analyzer.ResolveUnpivot`
+ *
+ * The type of the value column is derived from all value columns during
analysis once all values
+ * are resolved. All values' types have to be compatible, otherwise the result
value column cannot
+ * be assigned the individual values and an AnalysisException is thrown.
+ *
+ * @see
`org.apache.spark.sql.catalyst.analysis.TypeCoercionBase.UnpivotCoercion`
+ *
+ * @param ids Id columns
+ * @param values Value columns to unpivot
+ * @param variableColumnName Name of the variable column
+ * @param valueColumnName Name of the value column
+ * @param valueType Type of value column once known
+ * @param child Child operator
+ */
+case class Unpivot(
+ ids: Seq[NamedExpression],
+ values: Seq[NamedExpression],
+ variableColumnName: String,
+ valueColumnName: String,
+ valueType: Option[DataType],
Review Comment:
I think this does not bring more information to the logical plan, but more
of a bool flag to indicate if type coercion has been done or not. A simpler
idea is to have a method
```
def valuesTypeCoercioned: Boolean = values.forall(_.resolved) &&
values.nonEmpty && {
values.tail.foreall(v => v.dataType.sameType(values.head.dataType))
}
```
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