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Posted to issues@spark.apache.org by "Peter Toth (JIRA)" <ji...@apache.org> on 2018/09/28 19:29:00 UTC
[jira] [Comment Edited] (SPARK-25150) Joining DataFrames derived
from the same source yields confusing/incorrect results
[ https://issues.apache.org/jira/browse/SPARK-25150?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16632274#comment-16632274 ]
Peter Toth edited comment on SPARK-25150 at 9/28/18 7:28 PM:
-------------------------------------------------------------
[~nchammas], sorry for the late reply.
There is only one issue here. Please see zombie-analysis.py, 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}The root cause of the error you see when spark.sql.crossJoin.enabled=false (default) and the incorrect results when spark.sql.crossJoin.enabled=true is the same, the join condition is handled incorrectly.{color}
{color:#333333}Please see my PR's description for further details: [https://github.com/apache/spark/pull/22318]{color}
was (Author: petertoth):
[~nchammas], sorry for the late reply.
There is only one issue here. Please see zombie-analysis.py, 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: [https://github.com/apache/spark/pull/22318]{color}
> 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
> 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, 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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