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Minh Thai commented on SPARK-17368: ----------------------------------- [~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} 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} implicit def newProductEncoder[T <: Product : TypeTag]: Encoder[T] = Encoders.product[T] {code} _(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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org