ravwojdyla commented on code in PR #36430:
URL: https://github.com/apache/spark/pull/36430#discussion_r887126464
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -1593,6 +1593,35 @@ class Dataset[T] private[sql](
@scala.annotation.varargs
def select(col: String, cols: String*): DataFrame = select((col +:
cols).map(Column(_)) : _*)
+ /**
+ * Selects a set of columns via schema object.
+ */
+ def select(schema: StructType): DataFrame = {
+ val attrs = logicalPlan.output
+ val attrs_map = attrs.map { a => (a.name, a) }.toMap
+ val new_attrs = AttributeMap(schema.map { f =>
Review Comment:
@HyukjinKwon @jiangxb1987 one thing we could do is to validate that the
schema we are selecting/swapping by is compatible with the current schema
(including nested columns). Would this be an acceptable solution for you?
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