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

["Andreas Neumann (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-29358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17107775#comment-17107775 ] 

Andreas Neumann commented on SPARK-29358:
-----------------------------------------

I would like to put a vote for this feature
 * It makes life so much easier when you have multiple inputs with slightly varying schema, which is quite common for data that evolved over time.
 * The work-around described at the top where you explicitly add the missing columns is really cumbersome if the schema is large.
 * With the approach of an extra argument the compatibility concerns should be lifted. 

> 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: 3.1.0
>            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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