如何在 Apache Spark 1.0 中构建大型分布式 [稀疏] 矩阵？

Question

我有一个这样的 RDD：byUserHour: org.apache.spark.rdd.RDD[(String, String, Int)] 我想创建一个数据的稀疏矩阵，用于计算中值、平均值等。RDD 包含 row_id、column_id 和 value。我有两个包含 row_id 和 column_id 字符串的数组用于查找。

这是我的尝试：

import breeze.linalg._
val builder = new CSCMatrix.Builder[Int](rows=BCnUsers.value.toInt,cols=broadcastTimes.value.size)
byUserHour.foreach{x =>
  val row = userids.indexOf(x._1)
  val col = broadcastTimes.value.indexOf(x._2)
  builder.add(row,col,x._3)}
builder.result()

这是我的错误：

14/06/10 16:39:34 INFO DAGScheduler: Failed to run foreach at <console>:38
org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: breeze.linalg.CSCMatrix$Builder$mcI$sp
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1033)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1017)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:770)
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:713)
    at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:697)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1176)
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
    at akka.actor.ActorCell.invoke(ActorCell.scala:456)
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
    at akka.dispatch.Mailbox.run(Mailbox.scala:219)
    at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
    at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
    at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

我的数据集非常大，所以如果可能的话，我想这样做分布式。任何帮助将不胜感激。

---

进度更新：

CSCMartix 不适用于 Spark。但是，RowMatrix 扩展了 DistributedMatrix。 RowMatrix 确实有一个方法，computeColumnSummaryStatistics()，它应该计算我正在寻找的一些统计数据。我知道 MLlib 每天都在增长，所以我会关注更新，但同时我会尝试创建一个 RDD[Vector] 来提供 RowMatrix。注意到RowMatrix 是实验性的，它代表一个面向行的分布式矩阵，没有有意义的行索引。

Answer 1

从映射稍有不同的 byUserHour 开始，现在是 RDD[(String, (String, Int))] .，因为 RowMatrix 不保留 row_id 上的 groupByKey 行的顺序。也许将来我会弄清楚如何使用稀疏矩阵来做到这一点。

val byUser = byUserHour.groupByKey // RDD[(String, Iterable[(String, Int)])]
val times = countHour.map(x => x._1.split("\\+")(1)).distinct.collect.sortWith(_ < _)  // Array[String]
val broadcastTimes = sc.broadcast(times) // Broadcast[Array[String]]

val userMaps = byUser.mapValues { 
  x => x.map{
    case(time,cnt) => time -> cnt
  }.toMap
}  // RDD[(String, scala.collection.immutable.Map[String,Int])]


val rows = userMaps.map {
  case(u,ut) => (u.toDouble +: broadcastTimes.value.map(ut.getOrElse(_,0).toDouble))}       // RDD[Array[Double]]


import org.apache.spark.mllib.linalg.{Vector, Vectors}
val rowVec = rows.map(x => Vectors.dense(x)) // RDD[org.apache.spark.mllib.linalg.Vector]

import org.apache.spark.mllib.linalg.distributed._
val countMatrix = new RowMatrix(rowVec)
val stats = countMatrix.computeColumnSummaryStatistics()
val meanvec = stats.mean

Answer 2

您可以使用CoordinateMatrix：

import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}

val sparseMatrix = new CoordinateMatrix(byUserHour.map {
  case (row, col, data) => MatrixEntry(row, col, data) 
})