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https://issues.apache.org/jira/browse/SPARK-13337?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15172709#comment-15172709
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Zhong Wang edited comment on SPARK-13337 at 2/29/16 10:05 PM:
--------------------------------------------------------------
For an outer join, it is difficult to eliminate the null columns from the
result, because the null columns can come from both tables. The
`join-using-column` interface can automatically eliminate those columns, which
are very convenient. Sorry that I missed this point in my last reply.
was (Author: zwang):
For an outer join, it is difficult to eliminate the null columns from the
result. The `join-using-column` interface can automatically eliminate those
columns, which are very convenient. Sorry that I missed this point in my last
reply.
> DataFrame join-on-columns function should support null-safe equal
> -----------------------------------------------------------------
>
> Key: SPARK-13337
> URL: https://issues.apache.org/jira/browse/SPARK-13337
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.6.0
> Reporter: Zhong Wang
> Priority: Minor
>
> Currently, the join-on-columns function:
> {code}
> def join(right: DataFrame, usingColumns: Seq[String], joinType: String):
> DataFrame
> {code}
> performs a null-insafe join. It would be great if there is an option for
> null-safe join.
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