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https://issues.apache.org/jira/browse/SPARK-23025?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li resolved SPARK-23025.
-----------------------------
Resolution: Fixed
Fix Version/s: 2.3.0
> 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
> Fix For: 2.3.0
>
>
> 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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