Spark中的Access Array列

Question

Spark DataFrame包含Array [Double]类型的列.当我尝试在map()函数中将其返回时,它会抛出一个ClassCastException异常.以下Scala代码生成异常.

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)

例外是

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有人解释我为什么不起作用？我该怎么做呢？我使用Spark 1.5.1和scala 2.10.6

谢谢

Answer 1

ArrayType在被表示Row为scala.collection.mutable.WrappedArray.例如,您可以使用它来提取它

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

要么

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

甚至:

import scala.collection.mutable.WrappedArray

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

如果DataFrame相对较薄,则模式匹配可能是更好的方法:

import org.apache.spark.sql.Row

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

虽然你必须记住,序列的类型是未选中的.

在Spark> = 1.6中,您还可以使用Dataset如下:

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