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From "Nicholas Chammas (JIRA)" <>
Subject [jira] [Commented] (SPARK-25150) Joining DataFrames derived from the same source yields confusing/incorrect results
Date Fri, 21 Sep 2018 15:44:00 GMT


Nicholas Chammas commented on SPARK-25150:

Given that Spark appears to provide incorrect results when spark.sql.crossJoin.enabled is
set to true, shall we mark this as a correctness issue?

[~petertoth] / [~EeveeB] - Would you agree with that characterization?

> Joining DataFrames derived from the same source yields confusing/incorrect results
> ----------------------------------------------------------------------------------
>                 Key: SPARK-25150
>                 URL:
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.1
>            Reporter: Nicholas Chammas
>            Priority: Major
>         Attachments: output-with-implicit-cross-join.txt, output-without-implicit-cross-join.txt,
persons.csv, states.csv,
> 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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