If I want to store an Algebraic Data Type (ADT) (ie a Scala sealed trait hierarchy) within a Spark DataSet column, what is the best encoding strategy?
For example, if I have an ADT where the leaf types store different kinds of data:
sealed trait Occupation
case object SoftwareEngineer extends Occupation
case class Wizard(level: Int) extends Occupation
case class Other(description: String) extends Occupation
Whats the best way to construct a:
org.apache.spark.sql.DataSet[Occupation]
TL;DR There is no good solution right now, and given Spark SQL / Dataset implementation, it is unlikely there will be one in the foreseeable future.
You can use generic kryo or java encoder
val occupation: Seq[Occupation] = Seq(SoftwareEngineer, Wizard(1), Other("foo"))
spark.createDataset(occupation)(org.apache.spark.sql.Encoders.kryo[Occupation])
but is hardly useful in practice.
UDT API provides another possible approach as for now (Spark 1.6, 2.0, 2.1-SNAPSHOT) it is private and requires quite a lot boilerplate code (you can check o.a.s.ml.linalg.VectorUDT to see example implementation).
I have once dived deeply into the subject and created a repo showcasing all the approaches I have found could be useful.
Link: https://github.com/atais/spark-enum
Generally, zero323 is right, but you might find it useful to understand the full picture.
