scala spark UDF ClassCastException : WrappedArray$ofRef cannot be cast to [Lscala.Tuple2 - scala

Question

So I perform the necessary imports etc
import org.apache.spark.sql.functions.udf
import org.apache.spark.sql.types._
import spark.implicits._
then define some latlong points
val london = (1.0, 1.0)
val suburbia = (2.0, 2.0)
val southampton = (3.0, 3.0)
val york = (4.0, 4.0)
I then create a spark Dataframe like this and check that it works:
val exampleDF = Seq((List(london,suburbia),List(southampton,york)),
(List(york,london),List(southampton,suburbia))).toDF("AR1","AR2")
exampleDF.show()
the dataframe consists of the following types
DataFrame = [AR1: array<struct<_1:double,_2:double>>, AR2: array<struct<_1:double,_2:double>>]
I create a function to create a combination of points
// function to do what I want
val latlongexplode = (x: Array[(Double,Double)], y: Array[(Double,Double)]) => {
for (a <- x; b <-y) yield (a,b)
}
I check that the function works
latlongexplode(Array(london,york),Array(suburbia,southampton))
and it does. However after i create a UDF out of this function
// declare function into a Spark UDF
val latlongexplodeUDF = udf (latlongexplode)
when i try to use it in the spark dataframe I have created above like this:
exampleDF.withColumn("latlongexplode", latlongexplodeUDF($"AR1",$"AR2")).show(false)
I get a really long stacktrace which basically boils down to :
java.lang.ClassCastException:
scala.collection.mutable.WrappedArray$ofRef cannot be cast to
[Lscala.Tuple2;
org.apache.spark.sql.catalyst.expressions.ScalaUDF.$anonfun$f$3(ScalaUDF.scala:121)
org.apache.spark.sql.catalyst.expressions.ScalaUDF.eval(ScalaUDF.scala:1063)
org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:151)
org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:50)
org.apache.spark.sql.catalyst.expressions.InterpretedProjection.apply(Projection.scala:32)
scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:273)
How can I get this udf to work in Scala Spark? (im using 2.4 at the moment if this helps)
EDIT: it could be that the way I construct my example df has an issue.
But what I have as the actual data is an array (of unknown size) of lat/long tuples on each column.

Answer 1

When working with struct types in UDF, they are represented as Row objects, and array columns are represented as Seq. Also, you need to return a struct in the form of a Row, and you need to define a schema to return a struct.
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
val london = (1.0, 1.0)
val suburbia = (2.0, 2.0)
val southampton = (3.0, 3.0)
val york = (4.0, 4.0)
val exampleDF = Seq((List(london,suburbia),List(southampton,york)),
(List(york,london),List(southampton,suburbia))).toDF("AR1","AR2")
exampleDF.show(false)
+------------------------+------------------------+
|AR1 |AR2 |
+------------------------+------------------------+
|[[1.0, 1.0], [2.0, 2.0]]|[[3.0, 3.0], [4.0, 4.0]]|
|[[4.0, 4.0], [1.0, 1.0]]|[[3.0, 3.0], [2.0, 2.0]]|
+------------------------+------------------------+
val latlongexplode = (x: Seq[Row], y: Seq[Row]) => {
for (a <- x; b <- y) yield Row(a, b)
}
val udf_schema = ArrayType(
StructType(Seq(
StructField(
"city1",
StructType(Seq(
StructField("lat", FloatType),
StructField("long", FloatType)
))
),
StructField(
"city2",
StructType(Seq(
StructField("lat", FloatType),
StructField("long", FloatType)
))
)
))
)
// include this line if you see errors like
// "You're using untyped Scala UDF, which does not have the input type information."
// spark.sql("set spark.sql.legacy.allowUntypedScalaUDF = true")
val latlongexplodeUDF = udf(latlongexplode, udf_schema)
result = exampleDF.withColumn("latlongexplode", latlongexplodeUDF($"AR1",$"AR2"))
result.show(false)
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+
|AR1 |AR2 |latlongexplode |
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+
|[[1.0, 1.0], [2.0, 2.0]]|[[3.0, 3.0], [4.0, 4.0]]|[[[1.0, 1.0], [3.0, 3.0]], [[1.0, 1.0], [4.0, 4.0]], [[2.0, 2.0], [3.0, 3.0]], [[2.0, 2.0], [4.0, 4.0]]]|
|[[4.0, 4.0], [1.0, 1.0]]|[[3.0, 3.0], [2.0, 2.0]]|[[[4.0, 4.0], [3.0, 3.0]], [[4.0, 4.0], [2.0, 2.0]], [[1.0, 1.0], [3.0, 3.0]], [[1.0, 1.0], [2.0, 2.0]]]|
+------------------------+------------------------+--------------------------------------------------------------------------------------------------------+