[SPARK-25150] Joining DataFrames derived from the same source yields confusing/incorrect results

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.3.1, 2.4.3
• Fix Version/s: 3.2.0
• Component/s: SQL
• Labels: correctness
• Assignee: Unassigned
• Reporter: Nicholas Chammas
• Created: 17/Aug/18 18:06
• Updated: 14/Dec/21 21:28
• Resolved: 14/Dec/21 21:28

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:

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;

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.

Issue Links

• is duplicated by: SPARK-26231 Dataframes inner join on double datatype columns resulting in Cartesian product
• relates to: SPARK-20804 Join with null safe equality fails with AnalysisException
• relates to: SPARK-6459 Warn when Column API is constructing trivially true equality
• links to: [Github] Pull Request #22318 (peter-toth)
• links to: GitHub Pull Request #22318
• links to: Spark Dataset joinWith API giving wrong results
• links to: Why does spark think this is a cross/cartesian join