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https://issues.apache.org/jira/browse/SPARK-22335?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Carlos Bribiescas updated SPARK-22335:
--------------------------------------
Description:
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
{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`
I imagine its just lazily converting to typed DS instead of initially. So
either that could be prioritized or unioning of DF could be done with column
order taken into account. Again, this is speculation..
was:
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
{code}
So its inconsistent and a bug IMO. And I'm not sure of a workaround if you get
handed a DS witho
I imagine its just lazily converting to typed DS instead of initially. So
either that could be prioritized or unioning of DF could be done with column
order taken into account. Again, this is speculation..
> 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
> Priority: Minor
>
> 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
> {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`
> I imagine its just lazily converting to typed DS instead of initially. So
> either that could be prioritized or unioning of DF could be done with column
> order taken into account. Again, this is speculation..
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