Access Array column in Spark

Question

A Spark DataFrame contains a column of type Array[Double]. It throw a ClassCastException exception when I try to get it back in a map() function. The following Scala code generate an exception.

case class Dummy( x:Array[Double] )
val df = sqlContext.createDataFrame(Seq(Dummy(Array(1,2,3))))
val s = df.map( r => {
   val arr:Array[Double] = r.getAs[Array[Double]]("x")
   arr.sum
})
s.foreach(println)

The exception is

java.lang.ClassCastException: scala.collection.mutable.WrappedArray$ofRef cannot be cast to [D
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:24)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:23)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:890)
    at org.apache.spark.rdd.RDD$$anonfun$foreach$1$$anonfun$apply$28.apply(RDD.scala:890)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1848)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1848)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:88)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)

Cam somebody explain me why it does not work? what should I do instead? I am using Spark 1.5.1 and scala 2.10.6

Thanks

Crematory

Answer 1

ArrayType is represented in a Row as a scala.collection.mutable.WrappedArray. You can extract it using for example

val arr: Seq[Double] = r.getAs[Seq[Double]]("x")

or

val i: Int = ???
val arr = r.getSeq[Double](i)

or even:

import scala.collection.mutable.WrappedArray

val arr: WrappedArray[Double] = r.getAs[WrappedArray[Double]]("x")

If DataFrame is relatively thin then pattern matching could be a better approach:

import org.apache.spark.sql.Row

df.rdd.map{case Row(x: Seq[Double]) => (x.toArray, x.sum)}

although you have to keep in mind that the type of the sequence is unchecked.

In Spark >= 1.6 you can also use Dataset as follows:

df.select("x").as[Seq[Double]].rdd

Unpaged

Answer 2

This approach can also be considered :

  val tuples = Seq(("Abhishek", "Sengupta", Seq("MATH", "PHYSICS")))
  val dF = tuples.toDF("firstName", "lastName", "subjects")

  case class StudentInfo(fName: String, lName: String, subjects: Seq[String])

  val students = dF
    .collect()
    .map(row => StudentInfo(row.getString(0), row.getString(1), row.getSeq(2)))

  students.foreach(println)

Thay