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https://issues.apache.org/jira/browse/SPARK-20008?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15936847#comment-15936847
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Xiao Li commented on SPARK-20008:
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This sounds a general issue for our Spark SQL. For example,
{{spark.emptyDataFrame.distinct()}} also returns a non empty result set.
> hiveContext.emptyDataFrame.except(hiveContext.emptyDataFrame).count() returns
> 1
> -------------------------------------------------------------------------------
>
> Key: SPARK-20008
> URL: https://issues.apache.org/jira/browse/SPARK-20008
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2, 2.2.0
> Reporter: Ravindra Bajpai
> Assignee: Xiao Li
>
> hiveContext.emptyDataFrame.except(hiveContext.emptyDataFrame).count() yields
> 1 against expected 0.
> This was not the case with spark 1.5.2. This is an api change from usage
> point of view and hence I consider this as a bug. May be a boundary case, not
> sure.
> Work around - For now I check the counts != 0 before this operation. Not good
> for performance. Hence creating a jira to track it.
> As Young Zhang explained in reply to my mail -
> Starting from Spark 2, these kind of operation are implemented in left anti
> join, instead of using RDD operation directly.
> Same issue also on sqlContext.
> scala> spark.version
> res25: String = 2.0.2
> spark.sqlContext.emptyDataFrame.except(spark.sqlContext.emptyDataFrame).explain(true)
> == Physical Plan ==
> *HashAggregate(keys=[], functions=[], output=[])
> +- Exchange SinglePartition
> +- *HashAggregate(keys=[], functions=[], output=[])
> +- BroadcastNestedLoopJoin BuildRight, LeftAnti, false
> :- Scan ExistingRDD[]
> +- BroadcastExchange IdentityBroadcastMode
> +- Scan ExistingRDD[]
> This arguably means a bug. But my guess is liking the logic of comparing NULL
> = NULL, should it return true or false, causing this kind of confusion.
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