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https://issues.apache.org/jira/browse/SPARK-20660?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16023333#comment-16023333
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Michel Lemay commented on SPARK-20660:
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In my opinion, two schema should be considered the same if columns are the same
regardless of the order.
However, throwing an error would be significantly better than doing unexpected
things.
> Not able to merge Dataframes with different column orders
> ---------------------------------------------------------
>
> Key: SPARK-20660
> URL: https://issues.apache.org/jira/browse/SPARK-20660
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Michel Lemay
> Priority: Minor
>
> Union on two dataframes with different column orders is not supported and
> lead to hard to find issues.
> Here is an example showing the issue.
> {code}
> import org.apache.spark.sql.types._
> import org.apache.spark.sql.Row
> var inputSchema = StructType(StructField("key", StringType, nullable=true) ::
> StructField("value", IntegerType, nullable=true) :: Nil)
> var a = spark.createDataFrame(sc.parallelize((1 to 10)).map(x =>
> Row(x.toString, 555)), inputSchema)
> var b = a.select($"value" * 2 alias "value", $"key") // any transformation
> changing column order will show the problem.
> a.union(b).show
> // in order to make it work, we need to reorder columns
> val bCols = a.columns.map(aCol => b(aCol))
> a.union(b.select(bCols:_*)).show
> {code}
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