How to store custom objects in Dataset?

Question

According to Introducing Spark Datasets:

As we look forward to Spark 2.0, we plan some exciting improvements to Datasets, specifically: ... Custom

Answer 1

You can use UDTRegistration and then Case Classes, Tuples, etc... all work correctly with your User Defined Type!

Say you want to use a custom Enum:

trait CustomEnum { def value:String }
case object Foo extends CustomEnum  { val value = "F" }
case object Bar extends CustomEnum  { val value = "B" }
object CustomEnum {
  def fromString(str:String) = Seq(Foo, Bar).find(_.value == str).get
}

Register it like this:

// First define a UDT class for it:
class CustomEnumUDT extends UserDefinedType[CustomEnum] {
  override def sqlType: DataType = org.apache.spark.sql.types.StringType
  override def serialize(obj: CustomEnum): Any = org.apache.spark.unsafe.types.UTF8String.fromString(obj.value)
  // Note that this will be a UTF8String type
  override def deserialize(datum: Any): CustomEnum = CustomEnum.fromString(datum.toString)
  override def userClass: Class[CustomEnum] = classOf[CustomEnum]
}

// Then Register the UDT Class!
// NOTE: you have to put this file into the org.apache.spark package!
UDTRegistration.register(classOf[CustomEnum].getName, classOf[CustomEnumUDT].getName)

Then USE IT!

case class UsingCustomEnum(id:Int, en:CustomEnum)

val seq = Seq(
  UsingCustomEnum(1, Foo),
  UsingCustomEnum(2, Bar),
  UsingCustomEnum(3, Foo)
).toDS()
seq.filter(_.en == Foo).show()
println(seq.collect())

Say you want to use a Polymorphic Record:

trait CustomPoly
case class FooPoly(id:Int) extends CustomPoly
case class BarPoly(value:String, secondValue:Long) extends CustomPoly

... and the use it like this:

case class UsingPoly(id:Int, poly:CustomPoly)

Seq(
  UsingPoly(1, new FooPoly(1)),
  UsingPoly(2, new BarPoly("Blah", 123)),
  UsingPoly(3, new FooPoly(1))
).toDS

polySeq.filter(_.poly match {
  case FooPoly(value) => value == 1
  case _ => false
}).show()

You can write a custom UDT that encodes everything to bytes (I'm using java serialization here but it's probably better to instrument Spark's Kryo context).

First define the UDT class:

class CustomPolyUDT extends UserDefinedType[CustomPoly] {
  val kryo = new Kryo()

  override def sqlType: DataType = org.apache.spark.sql.types.BinaryType
  override def serialize(obj: CustomPoly): Any = {
    val bos = new ByteArrayOutputStream()
    val oos = new ObjectOutputStream(bos)
    oos.writeObject(obj)

    bos.toByteArray
  }
  override def deserialize(datum: Any): CustomPoly = {
    val bis = new ByteArrayInputStream(datum.asInstanceOf[Array[Byte]])
    val ois = new ObjectInputStream(bis)
    val obj = ois.readObject()
    obj.asInstanceOf[CustomPoly]
  }

  override def userClass: Class[CustomPoly] = classOf[CustomPoly]
}

Then register it:

// NOTE: The file you do this in has to be inside of the org.apache.spark package!
UDTRegistration.register(classOf[CustomPoly].getName, classOf[CustomPolyUDT].getName)

Then you can use it!

// As shown above:
case class UsingPoly(id:Int, poly:CustomPoly)

Seq(
  UsingPoly(1, new FooPoly(1)),
  UsingPoly(2, new BarPoly("Blah", 123)),
  UsingPoly(3, new FooPoly(1))
).toDS

polySeq.filter(_.poly match {
  case FooPoly(value) => value == 1
  case _ => false
}).show()