[jira] [Resolved] (SPARK-29358) Make unionByName optionally fill missing columns with nulls

["Hyukjin Kwon (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-29358?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-29358.
----------------------------------
    Resolution: Won't Fix

> Make unionByName optionally fill missing columns with nulls
> -----------------------------------------------------------
>
>                 Key: SPARK-29358
>                 URL: https://issues.apache.org/jira/browse/SPARK-29358
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 3.0.0
>            Reporter: Mukul Murthy
>            Priority: Major
>
> Currently, unionByName requires two DataFrames to have the same set of columns (even though the order can be different). It would be good to add either an option to unionByName or a new type of union which fills in missing columns with nulls. 
> {code:java}
> val df1 = Seq(1, 2, 3).toDF("x")
> val df2 = Seq("a", "b", "c").toDF("y")
> df1.unionByName(df2){code}
> This currently throws 
> {code:java}
> org.apache.spark.sql.AnalysisException: Cannot resolve column name "x" among (y);
> {code}
> Ideally, there would be a way to make this return a DataFrame containing:
> {code:java}
> +----+----+ 
> | x| y| 
> +----+----+ 
> | 1|null| 
> | 2|null| 
> | 3|null| 
> |null| a| 
> |null| b| 
> |null| c| 
> +----+----+
> {code}
> Currently the workaround to make this possible is by using unionByName, but this is clunky:
> {code:java}
> df1.withColumn("y", lit(null)).unionByName(df2.withColumn("x", lit(null)))
> {code}



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