下面是Spark admin在8080端口上运行的截图:
的数据
“Shuffle Read”和“Shuffle Write”参数对于此代码始终为空:
import org.apache.spark.SparkContext;
object first {
println("Welcome to the Scala worksheet")
val conf = new org.apache.spark.SparkConf()
.setMaster("local")
.setAppName("distances")
.setSparkHome("C:\\spark-1.1.0-bin-hadoop2.4\\spark-1.1.0-bin-hadoop2.4")
.set("spark.executor.memory", "2g")
val sc = new SparkContext(conf)
def euclDistance(userA: User, userB: User) = {
val subElements = (userA.features zip userB.features) map {
m => (m._1 - m._2) * (m._1 - m._2)
}
val summed = subElements.sum
val sqRoot = Math.sqrt(summed)
println("value is" + sqRoot)
((userA.name, userB.name), sqRoot)
}
case class User(name: String, features: Vector[Double])
def createUser(data: String) = {
val id = data.split(",")(0)
val splitLine = data.split(",")
val distanceVector = (splitLine.toList match {
case h :: t => t
}).map(m => m.toDouble).toVector
User(id, distanceVector)
}
val dataFile = sc.textFile("c:\\data\\example.txt")
val users = dataFile.map(m => createUser(m))
val cart = users.cartesian(users) //
val distances = cart.map(m => euclDistance(m._1, m._2))
//> distances : org.apache.spark.rdd.RDD[((String, String), Double)] = MappedR
//| DD[4] at map at first.scala:46
val d = distances.collect //
d.foreach(println) //> ((a,a),0.0)
//| ((a,b),0.0)
//| ((a,c),1.0)
//| ((a,),0.0)
//| ((b,a),0.0)
//| ((b,b),0.0)
//| ((b,c),1.0)
//| ((b,),0.0)
//| ((c,a),1.0)
//| ((c,b),1.0)
//| ((c,c),0.0)
//| ((c,),0.0)
//| ((,a),0.0)
//| ((,b),0.0)
//| ((,c),0.0)
//| ((,),0.0)
}
字符串
为什么“Shuffle Read”&“Shuffle Write”字段为空?可以调整上述代码以填充这些字段,以便了解如何
2条答案
按热度按时间r7xajy2e1#
Shuffling是指在多个Spark stage之间重新分配数据。“Shuffle Write”是在传输之前(通常在stage结束时)所有executors上所有写入的序列化数据的总和,而“Shuffle Read”是指在stage开始时所有executors上读取的序列化数据的总和。
你的程序只有一个阶段,由“collect”操作触发。不需要 Shuffle ,因为你只有一堆连续的map操作,它们在一个阶段中被流水线化。
试着看看这些幻灯片:http://de.slideshare.net/colorant/spark-shuffle-introduction
阅读原文第5章也会有帮助:http://people.csail.mit.edu/matei/papers/2012/nsdi_spark.pdf
zyfwsgd62#
我相信你必须在集群/分布式模式下运行你的应用程序才能看到任何Shuffle读或写值。通常“shuffle”由Spark操作的子集触发(例如,groupBy,join等)