[GitHub] [spark] ravwojdyla commented on a diff in pull request #36430: [WIP][SPARK-38904] Select by schema

[GitBox <gi...@apache.org>]

ravwojdyla commented on code in PR #36430:
URL: https://github.com/apache/spark/pull/36430#discussion_r863329395


##########
sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
##########
@@ -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:
   So this current code swaps the `dataType` without checking if it's safe. Do you or @jiangxb1987 have any tips how to safely handle nested data here?



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