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

["Apache Spark (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-19980?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15931650#comment-15931650 ] 

Apache Spark commented on SPARK-19980:
--------------------------------------

User 'maropu' has created a pull request for this issue:
https://github.com/apache/spark/pull/17347

> 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}
> 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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