[jira] [Issue Comment Deleted] (SPARK-17368) Scala value classes create encoder problems and break at runtime

["Minh Thai (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17368?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Minh Thai updated SPARK-17368:
------------------------------
    Comment: was deleted

(was: [~jodersky] I know that this is an old ticket but I still want to give some comments on making encoder for value classes. Even until today, there is no way to have a type constraint that targets value classes. However, I think we can make a [universal trait|https://docs.scala-lang.org/overviews/core/value-classes.html] called {{OpaqueValue}}^1^ to be used as an upper type bound in encoder. This means:
 - Any user-defined value class has to mixin {{OpaqueValue}}
 - An encoder can be created to target those value classes.

{code:java}
trait OpaqueValue extends Any
implicit def newValueClassEncoder[T <: Product with OpaqueValue : TypeTag] = ???

case class Id(value: Int) extends AnyVal with OpaqueValue
{code}
tested on my machine using Spark 2.1.0 and Scala 2.11.12, this doesn't clash with the existing encoder for case class
{code:java}
implicit def newProductEncoder[T <: Product : TypeTag]: Encoder[T] = Encoders.product[T]
{code}
If this is possible to implement. I think it can solve SPARK-20384 also.

_(1) the name is inspired from [Opaque Type|https://docs.scala-lang.org/sips/opaque-types.html] feature of Scala 3_)

> 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
>            Assignee: Jakob Odersky
>            Priority: Major
>             Fix For: 2.1.0
>
>
> 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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