I am using Spark 2.1.1 with Scala 2.11.6. I am getting the following error. I am not using any case classes.
java.lang.UnsupportedOperationException: No Encoder found for scala.collection.immutable.Set[String]
field (class: "scala.collection.immutable.Set", name: "_2")
field (class: "scala.Tuple2", name: "_2")
root class: "scala.Tuple2"
The following portion of code is where the stacktrace points at.
val tweetArrayRDD = nameDF.select("namedEnts", "text", "storylines")
.flatMap {
case Row(namedEnts: Traversable[(String, String)], text: String, storylines: Traversable[String]) =>
Option(namedEnts) match {
case Some(x: Traversable[(String, String)]) =>
//println("In flatMap:" + x + " ~~&~~ " + text + " ~~&~~ " + storylines)
namedEnts.map((_, (text, storylines.toSet)))
case _ => //println("In flatMap: blahhhh")
Traversable()
}
case _ => //println("In flatMap: fooooo")
Traversable()
}
.rdd.aggregateByKey((Set[String](), Set[String]()))((a, b) => (a._1 + b._1, a._2 ++ b._2), (a, b) => (a._1 ++ b._1, a._2 ++ b._2))
.map { (s: ((String, String), (Set[String], Set[String]))) => {
//println("In map: " + s)
(s._1, (s._2._1.toSeq, s._2._2.toSeq))
}}
The problem here is that Spark does not provide an encoder for Set
out-of-the-box (it does provide encoders for "primitives", Seqs, Arrays, and Products of other supported types).
You can either try using this excellent answer to create your own encoder for Set[String]
(more accurately, an encoder for the type you're using, Traversable[((String, String), (String, Set[String]))]
, which contains a Set[String]
), OR you can work-around this issue by using a Seq
instead of a Set
:
// ...
case Some(x: Traversable[(String, String)]) =>
//println("In flatMap:" + x + " ~~&~~ " + text + " ~~&~~ " + storylines)
namedEnts.map((_, (text, storylines.toSeq.distinct)))
// ...
(I'm using distinct
to immitate the Set
behavior; Can also try .toSet.toSeq
)
UPDATE: per your comment re Spark 1.6.2 - the difference is that in 1.6.2, Dataset.flatMap
returns an RDD
and not a Dataset
, therefore requires no encoding of the results returned from the function you supply; So, this indeed brings up another good workaround - you can easily simulate this behavior by explicitly switching to work with the RDD before the flatMap
operation:
nameDF.select("namedEnts", "text", "storylines")
.rdd
.flatMap { /*...*/ } // use your function as-is, it can return Set[String]
.aggregateByKey( /*...*/ )
.map( /*...*/ )