[jira] [Created] (SPARK-19980) Basic Dataset transformation on POJOs does not preserves nulls.

["Michel Lemay (JIRA)" <ji...@apache.org>]

Michel Lemay created SPARK-19980:
------------------------------------

             Summary: Basic Dataset transformation on POJOs does not preserves nulls.
                 Key: SPARK-19980
                 URL: https://issues.apache.org/jira/browse/SPARK-19980
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.1.0
            Reporter: Michel Lemay


Applying an identity map transformation on a statically typed Dataset with a POJO produces an unexpected result.

Given POJOs:
{code}
public class Stuff implements Serializable {
    private String name;
    public void setName(String name) { this.name = name; }
    public String getName() { return name; }
}

public class Outer implements Serializable {
    private String name;
    private Stuff stuff;
    public void setName(String name) { this.name = name; }
    public String getName() { return name; }
    public void setStuff(Stuff stuff) { this.stuff = stuff; }
    public Stuff getStuff() { return stuff; }
}
{code}

And test code:
{code}
val encoder = Encoders.bean(classOf[Outer])
val schema = encoder.schema
schema.printTreeString

val df = spark.read.schema(schema).json("d:\\stuff.json").as[Outer](encoder)
df.show()
df.map(x => x)(encoder).show()
{code}

Produces the result:

{code}
scala> val encoder = Encoders.bean(classOf[Outer])
encoder: org.apache.spark.sql.Encoder[pojos.Outer] = class[name[0]: string, stuff[0]: struct<name:string>]

scala> val schema = encoder.schema
schema: org.apache.spark.sql.types.StructType = StructType(StructField(name,StringType,true), StructField(stuff,StructType(StructField(name,StringType,true)),true))

scala> schema.printTreeString
root
 |-- name: string (nullable = true)
 |-- stuff: struct (nullable = true)
 |    |-- name: string (nullable = true)


scala> val df = spark.read.schema(schema).json("stuff.json").as[Outer](encoder)
df: org.apache.spark.sql.Dataset[pojos.Outer] = [name: string, stuff: struct<name: string>]

scala> df.show()
+----+-----+
|name|stuff|
+----+-----+
|  v1| null|
+----+-----+

scala> df.map(x => x)(encoder).show()
+----+------+
|name| stuff|
+----+------+
|  v1|[null]|
+----+------+
{code}

After identity transformation, `stuff` becomes an object with null values inside it instead of staying null itself.

Doing the same with case classes preserves the nulls:
{code}
scala> case class ScalaStuff(name: String)
defined class ScalaStuff

scala> case class ScalaOuter(name: String, stuff: ScalaStuff)
defined class ScalaOuter

scala> val encoder2 = Encoders.product[ScalaOuter]
encoder2: org.apache.spark.sql.Encoder[ScalaOuter] = class[name[0]: string, stuff[0]: struct<name:string>]

scala> val schema2 = encoder2.schema
schema2: org.apache.spark.sql.types.StructType = StructType(StructField(name,StringType,true), StructField(stuff,StructType(StructField(name,StringType,true)),true))

scala> schema2.printTreeString
root
 |-- name: string (nullable = true)
 |-- stuff: struct (nullable = true)
 |    |-- name: string (nullable = true)


scala>

scala> val df2 = spark.read.schema(schema2).json("stuff.json").as[ScalaOuter]
df2: org.apache.spark.sql.Dataset[ScalaOuter] = [name: string, stuff: struct<name: string>]

scala> df2.show()
+----+-----+
|name|stuff|
+----+-----+
|  v1| null|
+----+-----+


scala> df2.map(x => x).show()
+----+-----+
|name|stuff|
+----+-----+
|  v1| null|
+----+-----+
{code}

stuff.json:
{code}
{"name":"v1", "stuff":null }
{code}




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