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From "Peter Toth (JIRA)" <>
Subject [jira] [Commented] (SPARK-25150) Joining DataFrames derived from the same source yields confusing/incorrect results
Date Fri, 28 Sep 2018 19:17:00 GMT


Peter Toth commented on SPARK-25150:

[~nchammas], sorry for the late reply.

There is only one issue here. Please see, it contains 2 joins and both
joins define the condition explicitly, so setting spark.sql.crossJoin.enabled=true {color:#333333}should
not have any effect.{color}

{color:#333333}Simply the SQL statement should not fail, please see my PR's description for
further details: []{color}


> 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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