You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Michel Lemay (JIRA)" <ji...@apache.org> on 2017/05/08 20:22:04 UTC

[jira] [Created] (SPARK-20660) Not able to merge Dataframes with different column orders

Michel Lemay created SPARK-20660:
------------------------------------

             Summary: 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 by 2)).map(x => Row(x.toString, 555)), inputSchema)
var b = a.select($"value", $"key")  // any transformation changing column order will show the problem.
a.union(c).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}



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org