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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, 28 Sep 2018 19:37:00 GMT


Nicholas Chammas commented on SPARK-25150:

([~petertoth] - Seeing your comment edit now.) OK, so it seems the two problems I identified are
accurate, but they have a common root cause. Thanks for confirming.

[~srowen] - Given Peter's confirmation that the results with cross join enabled are incorrect,
I believe we should mark this as a correctness issue.

> 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: expected-output.txt, 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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