[jira] [Created] (SPARK-23025) DataSet with scala.Null causes Exception

["Daniel Davis (JIRA)" <ji...@apache.org>]

Daniel Davis created SPARK-23025:
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             Summary: DataSet with scala.Null causes Exception
                 Key: SPARK-23025
                 URL: https://issues.apache.org/jira/browse/SPARK-23025
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.2.1
            Reporter: Daniel Davis


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. 


h5. Minimal Exampe

{code}
case class Foo(foo: Int, bar: Null)
val ds = Seq(Foo(42, null)).toDS()
{code}

h5. Exception
{code}
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 
{code}

h5. 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.



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