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[SPARK-16853][SQL] fixes encoder error in DataSet typed select #14474

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[SPARK-16853][SQL] fixes encoder error in DataSet typed select #14474

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952ec92
fix SPARK-16853
clockfly Aug 3, 2016
d9b5a40
fix
clockfly Aug 4, 2016
3d90a68
fix MIMA
clockfly Aug 4, 2016
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4 changes: 3 additions & 1 deletion project/MimaExcludes.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,9 @@ object MimaExcludes {
lazy val v21excludes = v20excludes ++ {
Seq(
// [SPARK-16199][SQL] Add a method to list the referenced columns in data source Filter
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.sql.sources.Filter.references")
ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.sql.sources.Filter.references"),
// [SPARK-16853][SQL] Fixes encoder error in DataSet typed select
ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.sql.Dataset.select")
)
}

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4 changes: 4 additions & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
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Expand Up @@ -169,6 +169,10 @@ object ExpressionEncoder {
ClassTag(cls))
}

// Tuple1
def tuple[T](e: ExpressionEncoder[T]): ExpressionEncoder[Tuple1[T]] =
tuple(Seq(e)).asInstanceOf[ExpressionEncoder[Tuple1[T]]]

def tuple[T1, T2](
e1: ExpressionEncoder[T1],
e2: ExpressionEncoder[T2]): ExpressionEncoder[(T1, T2)] =
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20 changes: 11 additions & 9 deletions sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
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Expand Up @@ -1061,15 +1061,17 @@ class Dataset[T] private[sql](
* @since 1.6.0
*/
@Experimental
def select[U1: Encoder](c1: TypedColumn[T, U1]): Dataset[U1] = {
new Dataset[U1](
sparkSession,
Project(
c1.withInputType(
exprEnc.deserializer,
logicalPlan.output).named :: Nil,
logicalPlan),
implicitly[Encoder[U1]])
def select[U1](c1: TypedColumn[T, U1]): Dataset[U1] = {
implicit val encoder = c1.encoder
val project = Project(c1.withInputType(exprEnc.deserializer, logicalPlan.output).named :: Nil,
logicalPlan)

if (encoder.flat) {
new Dataset[U1](sparkSession, project, encoder)
} else {
// Flattens inner fields of U1
new Dataset[Tuple1[U1]](sparkSession, project, ExpressionEncoder.tuple(encoder)).map(_._1)
}
}

/**
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11 changes: 11 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
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Expand Up @@ -184,6 +184,17 @@ class DatasetSuite extends QueryTest with SharedSQLContext {
2, 3, 4)
}

test("SPARK-16853: select, case class and tuple") {
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how about typed select that returns case class or tuple?

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@cloud-fan

This follows existing test case name, for example, for select 2, current name is

test("select 2, primitive and tuple") {

https://github.com/apache/spark/pull/14474/files#diff-3836bd1fc5ae9c8b8ac4bdb2b9944159L196

val ds = Seq(("a", 1), ("b", 2), ("c", 3)).toDS()
checkDataset(
ds.select(expr("struct(_2, _2)").as[(Int, Int)]): Dataset[(Int, Int)],
(1, 1), (2, 2), (3, 3))

checkDataset(
ds.select(expr("named_struct('a', _1, 'b', _2)").as[ClassData]): Dataset[ClassData],
ClassData("a", 1), ClassData("b", 2), ClassData("c", 3))
}

test("select 2") {
val ds = Seq(("a", 1), ("b", 2), ("c", 3)).toDS()
checkDataset(
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