[jira] [Comment Edited] (SPARK-22335) Union for DataSet uses column order instead of types for union

["Liang-Chi Hsieh (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-22335?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16216248#comment-16216248 ] 

Liang-Chi Hsieh edited comment on SPARK-22335 at 10/24/17 3:36 AM:
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Can't {{unionByName}} solve it? 

{code}
scala> abDs.unionByName(baDs).show()
+------+------+
|     a|     b|
+------+------+
|aThing|bThing|
|aThing|bThing|
+------+------+
{code}



was (Author: viirya):
Can't {{unionByName}} solve it? 

{code]
scala> abDs.unionByName(baDs).show()
+------+------+
|     a|     b|
+------+------+
|aThing|bThing|
|aThing|bThing|
+------+------+
{code}


> Union for DataSet uses column order instead of types for union
> --------------------------------------------------------------
>
>                 Key: SPARK-22335
>                 URL: https://issues.apache.org/jira/browse/SPARK-22335
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: Carlos Bribiescas
>
> I see union uses column order for a DF. This to me is "fine" since they aren't typed.
> However, for a dataset which is supposed to be strongly typed it is actually giving the wrong result. If you try to access the members by name, it will use the order. Heres is a reproducible case. 2.2.0
> {code:java}
>   case class AB(a : String, b : String)
>   val abDf = sc.parallelize(List(("aThing","bThing"))).toDF("a", "b")
>   val baDf = sc.parallelize(List(("bThing","aThing"))).toDF("b", "a")
>   
>   abDf.union(baDf).show() // as linked ticket states, its "Not a problem"
>   
>   val abDs = abDf.as[AB]
>   val baDs = baDf.as[AB]
>   
>   abDs.union(baDs).show()  // This gives wrong result since a Dataset[AB] should be correctly mapped by type, not by column order
>   
>   abDs.union(baDs).map(_.a).show() // This gives wrong result since a Dataset[AB] should be correctly mapped by type, not by column order
>    abDs.union(baDs).rdd.take(2) // This also gives wrong result
>   baDs.map(_.a).show() // However, this gives the correct result, even though columns were out of order.
>   abDs.map(_.a).show() // This is correct too
>   baDs.select("a","b").as[AB].union(abDs).show() // This is the same workaround for linked issue, slightly modified.  However this seems wrong since its supposed to be strongly typed
>   
>   baDs.rdd.toDF().as[AB].union(abDs).show()  // This however gives correct result, which is logically inconsistent behavior
>   abDs.rdd.union(baDs.rdd).toDF().show() // Simpler example that gives correct result
> {code}
> So its inconsistent and a bug IMO.  And I'm not sure that the suggested work around is really fair, since I'm supposed to be getting of type `AB`.  More importantly I think the issue is bigger when you consider that it happens even if you read from parquet (as you would expect).  And that its inconsistent when going to/from rdd.
> I imagine its just lazily converting to typed DS instead of initially.  So either that typing could be prioritized to happen before the union or unioning of DF could be done with column order taken into account.  Again, this is speculation..



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