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https://issues.apache.org/jira/browse/SPARK-14850?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15418547#comment-15418547
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胡振宇 edited comment on SPARK-14850 at 8/12/16 9:02 AM:
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/*code is for spark 1.6.1*/
object Example{
def main (args:Array[String]){
val conf = new SparkConf.setAppname("Example")
val sc=new sparkContext(conf)
val sqlContext=new SQLContext(sc)
import sqlContext.implicts._
val count=sqlContext.sparkContext.parallelize(0,until 1e4.toInt,1).map{
i=>(i,Vectors.dense(Array.fill(1e6.toInt)(1.0)))
}.toDF().rdd.count() //at this step toDF can be used on
Spark1.6.1
}
}
so I am not able to test the simple serialization example
was (Author: fox19960207):
/*code is for spark 1.6.1*/
object Example{
def main (args:Array[String]){
val conf = new SparkConf.setAppname("Example")
val sc=new sparkContext(conf)
val sqlContext=new SQLContext(sc)
import sqlContext.implicts._
val count=sqlContext.sparkContext.parallelize(0,until 1e4.toInt,1).map{
i=>Test(i,Vectors.dense(Array.fill(1e6.toInt)(1.0)))
}.toDF().rdd.count() //at this step toDF can be used on
Spark1.6.1
}
}
so I am not able to test the simple serialization example
> VectorUDT/MatrixUDT should take primitive arrays without boxing
> ---------------------------------------------------------------
>
> Key: SPARK-14850
> URL: https://issues.apache.org/jira/browse/SPARK-14850
> 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 container.
> cc: [~cloud_fan] [~yhuai]
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