[jira] [Commented] (SPARK-32136) Spark producing incorrect groupBy results when key is a struct with nullable properties

["Jason Moore (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-32136?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17149073#comment-17149073 ] 

Jason Moore commented on SPARK-32136:
-------------------------------------

Here is a similar test, and why it's a problem for what I'm needing to do:


{noformat}
case class C(d: Double)
case class B(c: Option[C])
case class A(b: Option[B])
val df = Seq(
  A(None),
  A(Some(B(None))),
  A(Some(B(Some(C(1.0)))))
).toDF
val res = df.groupBy("b").agg(count("*"))

> res.show
+-------+--------+
|      b|count(1)|
+-------+--------+
|   [[]]|       2|
|[[1.0]]|       1|
+-------+--------+

> res.as[(Option[B], Long)].collect
java.lang.RuntimeException: Error while decoding: java.lang.NullPointerException: Null value appeared in non-nullable field:
- field (class: "scala.Double", name: "d")
- option value class: "C"
- field (class: "scala.Option", name: "c")
- option value class: "B"
- field (class: "scala.Option", name: "_1")
- root class: "scala.Tuple2"
If the schema is inferred from a Scala tuple/case class, or a Java bean, please try to use scala.Option[_] or other nullable types (e.g. java.lang.Integer instead of int/scala.Int).
newInstance(class scala.Tuple2)
{noformat}

Interestingly, and potentially usefully to know, that using an Int instead of a Double above works as expected.

> Spark producing incorrect groupBy results when key is a struct with nullable properties
> ---------------------------------------------------------------------------------------
>
>                 Key: SPARK-32136
>                 URL: https://issues.apache.org/jira/browse/SPARK-32136
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.0.0
>            Reporter: Jason Moore
>            Priority: Major
>
> I'm in the process of migrating from Spark 2.4.x to Spark 3.0.0 and I'm noticing a behaviour change in a particular aggregation we're doing, and I think I've tracked it down to how Spark is now treating nullable properties within the column being grouped by.
>  
> Here's a simple test I've been able to set up to repro it:
>  
> {code:scala}
> case class B(c: Option[Double])
> case class A(b: Option[B])
> val df = Seq(
> A(None),
> A(Some(B(None))),
> A(Some(B(Some(1.0))))
> ).toDF
> val res = df.groupBy("b").agg(count("*"))
> {code}
> Spark 2.4.6 has the expected result:
> {noformat}
> > res.show
> +-----+--------+
> |    b|count(1)|
> +-----+--------+
> |   []|       1|
> | null|       1|
> |[1.0]|       1|
> +-----+--------+
> > res.collect.foreach(println)
> [[null],1]
> [null,1]
> [[1.0],1]
> {noformat}
> But Spark 3.0.0 has an unexpected result:
> {noformat}
> > res.show
> +-----+--------+
> |    b|count(1)|
> +-----+--------+
> |   []|       2|
> |[1.0]|       1|
> +-----+--------+
> > res.collect.foreach(println)
> [[null],2]
> [[1.0],1]
> {noformat}
> Notice how it has keyed one of the values in be as `[null]`; that is, an instance of B with a null value for the `c` property instead of a null for the overall value itself.
> Is this an intended change?



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