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Tomasz Gawęda commented on SPARK-25150: --------------------------------------- [~nchammas] Maybe it's related to: [https://twitter.com/KurtFehlhauer/status/1030490707641790474] It looks like Spark is resolving columns to the ones in it's lineage, but not always in current schema > 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org