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From 胡振宇 (JIRA) <>
Subject [jira] [Commented] (SPARK-14850) VectorUDT/MatrixUDT should take primitive arrays without boxing
Date Fri, 12 Aug 2016 08:43:20 GMT


胡振宇 commented on SPARK-14850:

I try to run your code on spark1.6.1 but i found that "toDF" cannot be used in this example
Here are my code 
object Example{
def main (args:Array[String]){
  case class Test(num:Int,vector:Vector)
  val conf = new SparkConf.setAppname("Example")
  val sqlContext=new SQLContext(sc)
  import sqlContext.implicts._
  val temp=sqlContext.sparkContext.parallelize(0,until 1e4.toInt,1).map(i=>Test(i,Vectors.dense(Array.fill(1e6.toInt)(1.0)))).toDF()
//at this step toDF can be used I do

sc.parallelize(0 until 1e4.toInt, 1).map { i =>
  (i, Vectors.dense(Array.fill(1e6.toInt)(1.0)))

I even use sparkcontext but toDF cannot be used too

Do you have a solution to run the example on spark1.6.1? Thank you 


> VectorUDT/MatrixUDT should take primitive arrays without boxing
> ---------------------------------------------------------------
>                 Key: SPARK-14850
>                 URL:
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, SQL
>    Affects Versions: 1.5.2, 1.6.1, 2.0.0
>            Reporter: Xiangrui Meng
>            Assignee: Wenchen Fan
>            Priority: Critical
>             Fix For: 2.0.0
> In SPARK-9390, we switched to use GenericArrayData to store indices and values in vector/matrix
UDTs. However, GenericArrayData is not specialized for primitive types. This might hurt MLlib
performance badly. We should consider either specialize GenericArrayData or use a different
> cc: [~cloud_fan] [~yhuai]

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