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https://issues.apache.org/jira/browse/SPARK-26868?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16786381#comment-16786381
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Takeshi Yamamuro commented on SPARK-26868:
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Yea, you can do.
> Duplicate error message for implicit cartesian product in verbose explain
> -------------------------------------------------------------------------
>
> Key: SPARK-26868
> URL: https://issues.apache.org/jira/browse/SPARK-26868
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Takeshi Yamamuro
> Priority: Trivial
>
> Super trivial though, I just report this just in case (I think it would be
> nice if we could print this error message in a cleaner way):
> {code:java}
> scala> Seq(1).toDF("id").write.saveAsTable("t1")
> scala> Seq(1).toDF("id").write.saveAsTable("t2")
> scala> sql("SELECT * FROM t1 JOIN t2").explain(true)
> == Parsed Logical Plan ==
> 'Project [*]
> +- 'Join Inner
> :- 'UnresolvedRelation `t1`
> +- 'UnresolvedRelation `t2`
> == Analyzed Logical Plan ==
> id: int, id: int
> Project [id#14, id#15]
> +- Join Inner
> :- SubqueryAlias `default`.`t1`
> : +- Relation[id#14] parquet
> +- SubqueryAlias `default`.`t2`
> +- Relation[id#15] parquet
> == Optimized Logical Plan ==
> org.apache.spark.sql.AnalysisException: Detected implicit cartesian product
> for INNER join between logical plans
> Relation[id#14] parquet
> and
> Relation[id#15] parquet
> 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;
> == Physical Plan ==
> org.apache.spark.sql.AnalysisException: Detected implicit cartesian product
> for INNER join between logical plans
> Relation[id#14] parquet
> and
> Relation[id#15] parquet
> 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}
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