EnricoMi commented on a change in pull request #26969: [SPARK-30319][SQL] Add a
stricter version of `as[T]`
URL: https://github.com/apache/spark/pull/26969#discussion_r366256637
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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@@ -495,6 +495,25 @@ class Dataset[T] private[sql](
select(newCols : _*)
}
+ /**
+ * Returns a new Dataset where each record has been mapped on to the
specified type.
+ * This only supports `U` being a class. Fields for the class will be mapped
to columns of the
+ * same name (case sensitivity is determined by `spark.sql.caseSensitive`).
+ *
+ * If the schema of the Dataset does not match the desired `U` type, you can
use `select`
+ * along with `alias` or `as` to rearrange or rename as required.
+ *
+ * This method eagerly projects away any columns that are not present in the
specified class.
+ * It further guarantees the order of columns as well as data types to match
`U`.
+ *
+ * @group basic
+ * @since 3.0.0
+ */
+ def toDS[U : Encoder]: Dataset[U] = {
+ val columns = implicitly[Encoder[U]].schema.fields.map(f =>
col(f.name).cast(f.dataType))
Review comment:
As the documentation of that method says, this only supports U being a
class. Tuples or structs are not supported. Those would require knowledge that
only the encoder does have.
For classes a simple projection is sufficient, which is very cheap. An
encoder round trip should be avoided in this use case. The stricter as method
could be extended later to other encoders and kinds of Us, but I think classes
are a wide use case that justify adding it already.
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