Major Description
When creating a DataSet over a case class containing a field of type scala.Null, there is an exception thrown. As far as I can see, spark sql would support a Schema(NullType, true), but it fails inside the schemaFor function with a MatchError.
I would expect spark to return a DataSet with a NullType for that field.
Minimal Exampe
case class Foo(foo: Int, bar: Null) val ds = Seq(Foo(42, null)).toDS()
Exception
scala.MatchError: scala.Null (of class scala.reflect.internal.Types$ClassNoArgsTypeRef) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:713) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$schemaFor$1.apply(ScalaReflection.scala:704) at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56) at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809) at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39) at org.apache.spark.sql.catalyst.ScalaReflection$.schemaFor(ScalaReflection.scala:703) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$deserializerFor$1$$anonfun$9.apply(ScalaReflection.scala:391) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$deserializerFor$1$$anonfun$9.apply(ScalaReflection.scala:390) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:381) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:285) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$deserializerFor$1.apply(ScalaReflection.scala:390) at org.apache.spark.sql.catalyst.ScalaReflection$$anonfun$org$apache$spark$sql$catalyst$ScalaReflection$$deserializerFor$1.apply(ScalaReflection.scala:148) at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:56) at org.apache.spark.sql.catalyst.ScalaReflection$class.cleanUpReflectionObjects(ScalaReflection.scala:809) at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:39) at org.apache.spark.sql.catalyst.ScalaReflection$.org$apache$spark$sql$catalyst$ScalaReflection$$deserializerFor(ScalaReflection.scala:148) at org.apache.spark.sql.catalyst.ScalaReflection$.deserializerFor(ScalaReflection.scala:136) at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:72) at org.apache.spark.sql.Encoders$.product(Encoders.scala:275) at org.apache.spark.sql.LowPrioritySQLImplicits$class.newProductEncoder(SQLImplicits.scala:233) at org.apache.spark.sql.SQLImplicits.newProductEncoder(SQLImplicits.scala:33) ... 42 elided
Background Info
To handle our data in a type-safe fashion, we have generated AVRO schemas and corresponding scala case classes for our domain data. As some fields only contain null values, this results in fields with scala.Null as a type. Moving our pipeline to DataSets/structured streaming, case classes with Null types begin to give problems, even trough NullType is known to spark SQL.
