Estou tentando escrever um UDF que retorna um tipo complexo:

private val toPrice = UDF1<String, Map<String, String>> { s ->
    val elements = s.split(" ")
    mapOf("value" to elements[0], "currency" to elements[1])
}


val type = DataTypes.createStructType(listOf(
        DataTypes.createStructField("value", DataTypes.StringType, false),
        DataTypes.createStructField("currency", DataTypes.StringType, false)))
df.sqlContext().udf().register("toPrice", toPrice, type)

mas sempre que eu uso isso:

df = df.withColumn("price", callUDF("toPrice", col("price")))

Eu recebo um erro enigmático:

Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$28: (string) => struct<value:string,currency:string>)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun A função deve retornar um objeto da classe$anon$1.hasNext(WholeStageCodegenExec.scala:614)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternalmas sempre que eu uso isso:$anonfun$apply$25.apply(RDD.scala:830)
    at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternalmas sempre que eu uso isso:$anonfun$apply$25.apply(RDD.scala:830)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:109)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: scala.MatchError: {value=138.0, currency=USD} (of class java.util.LinkedHashMap)
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:236)
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:231)
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:103)
    at org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:379)
    ... 19 more

Eu tentei usar um tipo de dados personalizado:

class Price(val value: Double, val currency: String) : Serializable

com um UDF que retorna esse tipo:

private val toPrice = UDF1<String, Price> { s ->
    val elements = s.split(" ")
    Price(elements[0].toDouble(), elements[1])
}

mas depois eu recebo outroMatchError que reclama pelaPrice tipo.

Como escrevo corretamente um UDF que pode retornar um tipo complexo?