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https://issues.apache.org/jira/browse/SPARK-25150?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16910691#comment-16910691
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Nicholas Chammas edited comment on SPARK-25150 at 8/19/19 7:39 PM:
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I haven't been able to boil down the reproduction further, but I'm updating
this issue to confirm that it is still present as of Spark 2.4.3 and,
particularly in the case where cross joins are enabled, it appears to be a
correctness issue.
My original attachments still capture the problem. These are the inputs:
* [^persons.csv]
* [^states.csv]
* [^zombie-analysis.py]
And here are the outputs:
* [^expected-output.txt]
* [^output-without-implicit-cross-join.txt]
* [^output-with-implicit-cross-join.txt]
was (Author: nchammas):
I haven't been able to boil down the reproduction further, but I'm updating
this issue to confirm that it is still present as of Spark 2.4.3 and,
particularly in the case where cross joins are enabled, it appears to be a
correctness issue.
My original attachments still capture the problem. These are the inputs:
* !persons.csv|width=7,height=7,align=absmiddle!
* !states.csv|width=7,height=7,align=absmiddle!
* [^zombie-analysis.py] !zombie-analysis.py|width=7,height=7,align=absmiddle!
And here are the outputs:
* [^expected-output.txt]
* [^output-without-implicit-cross-join.txt]
* [^output-with-implicit-cross-join.txt]
> Joining DataFrames derived from the same source yields confusing/incorrect
> results
> ----------------------------------------------------------------------------------
>
> Key: SPARK-25150
> URL: https://issues.apache.org/jira/browse/SPARK-25150
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.3.1, 2.4.3
> Reporter: Nicholas Chammas
> Priority: Major
> Labels: correctness
> Attachments: expected-output.txt,
> output-with-implicit-cross-join.txt, output-without-implicit-cross-join.txt,
> persons.csv, states.csv, zombie-analysis.py
>
>
> I have two DataFrames, A and B. From B, I have derived two additional
> DataFrames, B1 and B2. When joining A to B1 and B2, I'm getting a very
> confusing error:
> {code:java}
> Join condition is missing or trivial.
> Either: use the CROSS JOIN syntax to allow cartesian products between these
> relations, or: enable implicit cartesian products by setting the configuration
> variable spark.sql.crossJoin.enabled=true;
> {code}
> Then, when I configure "spark.sql.crossJoin.enabled=true" as instructed,
> Spark appears to give me incorrect answers.
> I am not sure if I am missing something obvious, or if there is some kind of
> bug here. The "join condition is missing" error is confusing and doesn't make
> sense to me, and the seemingly incorrect output is concerning.
> I've attached a reproduction, along with the output I'm seeing with and
> without the implicit cross join enabled.
> I realize the join I've written is not "correct" in the sense that it should
> be left outer join instead of an inner join (since some of the aggregates are
> not available for all states), but that doesn't explain Spark's behavior.
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