cloud-fan 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_r367908939
##########
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:
I'm OK to make the scope small and only support common cases like case
class. But we must make sure the implementation is defensive and we give a
clear better error message for unsupported cases.
Now this just throws attribute not found exception if the encoder is
`Encoders.javaSerialization`.
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