I am trying to figure out if I can work with Kotlin and Spark, and use the former's data classes instead of Scala's case classes.
I have the following data class:
data class Transaction(var context: String = "", var epoch: Long = -1L, var items: HashSet<String> = HashSet()) :
Serializable {
companion object {
@JvmStatic
private val serialVersionUID = 1L
}
}
And the relevant part of the main routine looks like this:
val transactionEncoder = Encoders.bean(Transaction::class.java)
val transactions = inputDataset
.groupByKey(KeyExtractor(), KeyExtractor.getKeyEncoder())
.mapGroups(TransactionCreator(), transactionEncoder)
.collectAsList()
transactions.forEach { println("collected Transaction=$it") }
With TransactionCreator defined as:
class TransactionCreator : MapGroupsFunction<Tuple2<String, Timestamp>, Row, Transaction> {
companion object {
@JvmStatic
private val serialVersionUID = 1L
}
override fun call(key: Tuple2<String, Timestamp>, values: MutableIterator<Row>): Transaction {
val seq = generateSequence { if (values.hasNext()) values.next().getString(2) else null }
val items = seq.toCollection(HashSet())
return Transaction(key._1, key._2.time, items).also { println("inside call Transaction=$it") }
}
}
However, I think I'm running into some sort of serialization problem, because the set ends up empty after collection. I see the following output:
inside call Transaction=Transaction(context=context1, epoch=1000, items=[c])
inside call Transaction=Transaction(context=context1, epoch=0, items=[a, b])
collected Transaction=Transaction(context=context1, epoch=0, items=[])
collected Transaction=Transaction(context=context1, epoch=1000, items=[])
I've tried a custom KryoRegistrator to see if it was a problem with Kotlin's HashSet:
class MyRegistrator : KryoRegistrator {
override fun registerClasses(kryo: Kryo) {
kryo.register(HashSet::class.java, JavaSerializer()) // kotlin's HashSet
}
}
But it doesn't seem to help. Any other ideas?
Full code here.
It does seem to be a serialization issue.
The documentation of Encoders.bean states (Spark v2.4.0):
collection types: only array and java.util.List currently, map support is in progress
Porting the Transaction data class to Java and changing items to a java.util.List seems to help.