cloud-fan commented on code in PR #37011:
URL: https://github.com/apache/spark/pull/37011#discussion_r1264788328
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sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala:
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@@ -464,6 +464,29 @@ class Dataset[T] private[sql](
*/
def as[U : Encoder]: Dataset[U] = Dataset[U](sparkSession, logicalPlan)
+ /**
+ * Returns a new DataFrame where each row is reconciled to match the
specified schema. Spark will:
+ * <ul>
+ * <li>Reorder columns and/or inner fields by name to match the specified
schema.</li>
+ * <li>Project away columns and/or inner fields that are not needed by the
specified schema.
+ * Missing columns and/or inner fields (present in the specified schema
but not input DataFrame)
+ * lead to failures.</li>
+ * <li>Cast the columns and/or inner fields to match the data types in the
specified schema, if
+ * the types are compatible, e.g., numeric to numeric (error if
overflows), but not string to
+ * int.</li>
+ * <li>Carry over the metadata from the specified schema, while the
columns and/or inner fields
+ * still keep their own metadata if not overwritten by the specified
schema.</li>
+ * <li>Fail if the nullability are not compatible. For example, the column
and/or inner field is
+ * nullable but the specified schema requires them to be not nullable.</li>
+ * </ul>
+ *
+ * @group basic
+ * @since 3.4.0
+ */
+ def as(schema: StructType): DataFrame = withPlan {
+ Project.matchSchema(logicalPlan, schema, sparkSession.sessionState.conf)
Review Comment:
Good question. char/varchar is not officially supported in the type system
so I think it's OK to ignore char/varchar here.
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