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