[jira] [Created] (SPARK-28630) Union fails when column order is different

["nirav patel (JIRA)" <ji...@apache.org>]

nirav patel created SPARK-28630:
-----------------------------------

             Summary: Union fails when column order is different
                 Key: SPARK-28630
                 URL: https://issues.apache.org/jira/browse/SPARK-28630
             Project: Spark
          Issue Type: Improvement
          Components: Spark Core
    Affects Versions: 2.2.3
            Reporter: nirav patel


I am trying to union 2 df which has same number of columns and types but in different order. It fails.

      val df1 = sparkSession.sparkContext.parallelize(List(

        (1, 5, true),

        (2, 3, false),

        (4, 4, true)

      )).toDF("id","age", "vaccinated")

 

      val df3 = sparkSession.sparkContext.parallelize(List(

        (1,  true, 6),

        (2, false, 3),

        (3, false, 2)

      )).toDF("id","vaccinated", "age")

df1.union(df3)

 

Actual output:

org.apache.spark.sql.AnalysisException: Union can only be performed on tables with the same number of columns

Expected output:

 

It should read schema (column names and/Or types ). I can see sometime you want to ignore column name and just want to do merge based on types. So May be introduce an option whether to use names followed by type or just type while doing merge.



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