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From "Davies Liu (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-18055) Dataset.flatMap can't work with types from customized jar
Date Fri, 21 Oct 2016 17:46:58 GMT
Davies Liu created SPARK-18055:
----------------------------------

             Summary: Dataset.flatMap can't work with types from customized jar
                 Key: SPARK-18055
                 URL: https://issues.apache.org/jira/browse/SPARK-18055
             Project: Spark
          Issue Type: Bug
          Components: SQL
    Affects Versions: 2.0.1
            Reporter: Davies Liu


Try to apply flatMap() on Dataset column which of of type
com.A.B

Here's a schema of a dataset:

root
 |-- id: string (nullable = true)
 |-- outputs: array (nullable = true)
 |    |-- element: string

flatMap works on RDD

{code}
 ds.rdd.flatMap(_.outputs)
{code}

flatMap doesnt work on dataset and gives the following error
{code}
ds.flatMap(_.outputs)
{code}

The exception:
{code}
scala.ScalaReflectionException: class com.relateiq.company.CompanySourceOutputMessage in JavaMirror
… not found
    at scala.reflect.internal.Mirrors$RootsBase.staticClass(Mirrors.scala:123)
    at scala.reflect.internal.Mirrors$RootsBase.staticClass(Mirrors.scala:22)
    at line189424fbb8cd47b3b62dc41e417841c159.$read$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$iw$$typecreator3$1.apply(<console>:51)
    at scala.reflect.api.TypeTags$WeakTypeTagImpl.tpe$lzycompute(TypeTags.scala:232)
    at scala.reflect.api.TypeTags$WeakTypeTagImpl.tpe(TypeTags.scala:232)
    at org.apache.spark.sql.SQLImplicits$$typecreator9$1.apply(SQLImplicits.scala:125)
    at scala.reflect.api.TypeTags$WeakTypeTagImpl.tpe$lzycompute(TypeTags.scala:232)
    at scala.reflect.api.TypeTags$WeakTypeTagImpl.tpe(TypeTags.scala:232)
    at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder$.apply(ExpressionEncoder.scala:49)
    at org.apache.spark.sql.SQLImplicits.newProductSeqEncoder(SQLImplicits.scala:125)
{code}

Spoke to Michael Armbrust and he confirmed it as a Dataset bug.

There is a workaround using explode()
{code}
ds.select(explode(col("outputs")))
{code}



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