[jira] [Commented] (SPARK-18055) Dataset.flatMap can't work with types from customized jar

["Song Jun (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-18055?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15646477#comment-15646477 ] 

Song Jun commented on SPARK-18055:
----------------------------------

[~davies] I can't reproduce it on the master branch or on databricks(Spark 2.0.1-db1 (Scala 2.11)),my code:

import scala.collection.mutable.ArrayBuffer
case class MyData(id: String,arr: Seq\[String])
val myarr = ArrayBuffer\[MyData]()
for(i <- 20 to 30){
	val arr = ArrayBuffer\[String]()
	for(j <- 1 to 10) {
		arr += (i+j).toString
	}

	val mydata = new MyData(i.toString,arr)
	myarr += mydata
}

val rdd = spark.sparkContext.makeRDD(myarr)
val ds = rdd.toDS

ds.rdd.flatMap(_.arr)
ds.flatMap(_.arr)

there is no exception, it has fixed? or My code is wrong?

> 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
>         Attachments: test-jar_2.11-1.0.jar
>
>
> Try to apply flatMap() on Dataset column which of of type
> com.A.B
> Here's a schema of a dataset:
> {code}
> root
>  |-- id: string (nullable = true)
>  |-- outputs: array (nullable = true)
>  |    |-- element: string
> {code}
> 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.A.B 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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