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Date: Wed, 7 Sep 2016 18:27:20 +0000 (UTC)
From: "Jakob Odersky (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.13002196.1472771529000.511031.1473272840468@Atlassian.JIRA>
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Subject: [jira] [Commented] (SPARK-17368) Scala value classes create encoder
 problems and break at runtime
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archived-at: Wed, 07 Sep 2016 18:27:22 -0000


    [ https://issues.apache.org/jira/browse/SPARK-17368?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15471428#comment-15471428 ] 

Jakob Odersky commented on SPARK-17368:
---------------------------------------

Hmm, you're right my assumption was of using only value classes in the beginning and at the end was too naive.

[~srowen], how likely do you think it is that we can include a meta-encoder in Spark? It could be included in the form of an optional import. Since the existing encoders/ScalaReflection framework already use runtime-reflection, my guess is that adding compile-time reflection will not be too difficult.

> Scala value classes create encoder problems and break at runtime
> ----------------------------------------------------------------
>
>                 Key: SPARK-17368
>                 URL: https://issues.apache.org/jira/browse/SPARK-17368
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core, SQL
>    Affects Versions: 1.6.2, 2.0.0
>         Environment: JDK 8 on MacOS
> Scala 2.11.8
> Spark 2.0.0
>            Reporter: Aris Vlasakakis
>
> Using Scala value classes as the inner type for Datasets breaks in Spark 2.0 and 1.6.X.
> This simple Spark 2 application demonstrates that the code will compile, but will break at runtime with the error. The value class is of course *FeatureId*, as it extends AnyVal.
> {noformat}
> Exception in thread "main" java.lang.RuntimeException: Error while encoding: java.lang.RuntimeException: Couldn't find v on int
> assertnotnull(input[0, int, true], top level non-flat input object).v AS v#0
> +- assertnotnull(input[0, int, true], top level non-flat input object).v
>    +- assertnotnull(input[0, int, true], top level non-flat input object)
>       +- input[0, int, true]".
>         at org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:279)
>         at org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:421)
>         at org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:421)
> {noformat}
> Test code for Spark 2.0.0:
> {noformat}
> import org.apache.spark.sql.{Dataset, SparkSession}
> object BreakSpark {
>   case class FeatureId(v: Int) extends AnyVal
>   def main(args: Array[String]): Unit = {
>     val seq = Seq(FeatureId(1), FeatureId(2), FeatureId(3))
>     val spark = SparkSession.builder.getOrCreate()
>     import spark.implicits._
>     spark.sparkContext.setLogLevel("warn")
>     val ds: Dataset[FeatureId] = spark.createDataset(seq)
>     println(s"BREAK HERE: ${ds.count}")
>   }
> }
> {noformat}


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