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https://issues.apache.org/jira/browse/SPARK-11949?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15031561#comment-15031561
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Apache Spark commented on SPARK-11949:
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User 'viirya' has created a pull request for this issue:
https://github.com/apache/spark/pull/10038
> Query on DataFrame from cube gives wrong results
> ------------------------------------------------
>
> Key: SPARK-11949
> URL: https://issues.apache.org/jira/browse/SPARK-11949
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.1
> Reporter: Veli Kerim Celik
> Labels: dataframe, sql
>
> {code:title=Reproduce bug|borderStyle=solid}
> case class fact(date: Int, hour: Int, minute: Int, room_name: String, temp:
> Double)
> val df0 = sc.parallelize(Seq
> (
> fact(20151123, 18, 35, "room1", 18.6),
> fact(20151123, 18, 35, "room2", 22.4),
> fact(20151123, 18, 36, "room1", 17.4),
> fact(20151123, 18, 36, "room2", 25.6)
> )).toDF()
> val cube0 = df0.cube("date", "hour", "minute", "room_name").agg(Map
> (
> "temp" -> "avg"
> ))
> cube0.where("date IS NULL").show()
> {code}
> The query result is empty. It should not be, because cube0 contains the value
> null several times in column 'date'. The issue arises because the cube
> function reuses the schema information from df0. If I change the type of
> parameters in the case class to Option[T] the query gives correct results.
> Solution: The cube function should change the schema by changing the nullable
> property to true, for the columns (dimensions) specified in the method call
> parameters.
> I am new at Scala and Spark. I don't know how to implement this. Somebody
> please do.
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