Carlos Bribiescas created SPARK-22335:
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Summary: 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
This isn't quite the issue I'm facing, but solving this issue will fix my
issue. (probably)
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 this ticket states, its "Not a problem"
val abDs = abDf.as[AB]
val baDs = baDf.as[AB]
abDs.union(baDs).show()
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
{code}
So its inconsistent and a bug IMO.
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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