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https://issues.apache.org/jira/browse/SPARK-29358?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17072294#comment-17072294
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Hyukjin Kwon commented on SPARK-29358:
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I am good with adding this behaviour if other committers think it's useful.
Yeah, it's really extending rather than breaking.
My concern was:
- users might rely on the exception when the schema is mismatched
- workarounds might be possible with other combinations of APIs
After rethinking about this, the former could be guarded by a parameter or
switch. The latter concern would need to extend the functionality of
{{StructType.merge}} so I am not so against it.
Let me reopen the ticket.
> 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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