[
https://issues.apache.org/jira/browse/SPARK-25150?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17459494#comment-17459494
]
Nicholas Chammas commented on SPARK-25150:
------------------------------------------
I re-ran my test (described in the issue description + summarized in my comment
just above) on Spark 3.2.0, and this issue appears to be resolved! Whether with
cross joins enabled or disabled, I now get the correct results.
Obviously, I have no clue what change since Spark 2.4.3 (the last time I reran
this test) was responsible for the fix.
But to be clear, in case anyone wants to reproduce my test:
# Download all 6 files attached to this issue into a folder.
# Then, from within that folder, run {{spark-submit zombie-analysis.py}} and
inspect the output.
# Then, enable cross joins (commented out on line 9), rerun the script, and
reinspect the output.
# Compare the final bit of output from both runs against
{{{}expected-output.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.
--
This message was sent by Atlassian Jira
(v8.20.1#820001)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
