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https://issues.apache.org/jira/browse/SPARK-25150?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nicholas Chammas updated SPARK-25150:
-------------------------------------
Description:
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.
was:
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.
> 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: 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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