Question

如果我增加word2vec模型的模型大小，我会在log中开始出现这种异常：

org.apache.spark.shuffle.MetadataFetchFailedException: Missing an output location for shuffle 6
    at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:542)
    at org.apache.spark.MapOutputTracker$$anonfun$org$apache$spark$MapOutputTracker$$convertMapStatuses$2.apply(MapOutputTracker.scala:538)
    at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
    at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
    at org.apache.spark.MapOutputTracker$.org$apache$spark$MapOutputTracker$$convertMapStatuses(MapOutputTracker.scala:538)
    at org.apache.spark.MapOutputTracker.getMapSizesByExecutorId(MapOutputTracker.scala:155)
    at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:47)
    at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
    at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:96)
    at org.apache.spark.rdd.CoalescedRDD$$anonfun$compute$1.apply(CoalescedRDD.scala:95)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:927)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    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)

我试着写自己的＆＃34;保存模型＆＃34;看起来像这样的版本：

  def save(model: Word2VecModel, sc: SparkContext, path: String): Unit = {

    println("Saving model as CSV ..")

    val vectorSize = model.getVectors.values.head.size

    println("vectorSize="+vectorSize)

    val SEPARATOR_TOKEN = " "    
    val dataArray = model.getVectors.toSeq.map { case (w, v) => Data(w, v) }

    println("Got dataArray ..")
    println("parallelize(dataArray, 10)")
    val par = sc.parallelize(dataArray, 10)
          .map(d => {

            val sb = new mutable.StringBuilder()
            sb.append(d.word)
            sb.append(SEPARATOR_TOKEN)

            for(v <- d.vector) {
              sb.append(v)
              sb.append(SEPARATOR_TOKEN)
            }
            sb.setLength(sb.length - 1)
            sb.append("\n")
            sb.toString()
          })
    println("repartition(1)")
    val rep = par.repartition(1)
    println("collect()")
    val vectorsAsString = rep.collect()

    println("Collected serialized vectors ..")    

    val cfile = new mutable.StringBuilder()

    cfile.append(vectorsAsString.length)
    cfile.append(" ")
    cfile.append(vectorSize)
    cfile.append("\n")

    val sb = new StringBuilder
    sb.append("word,")
    for(i <- 0 until vectorSize) {
      sb.append("v")
      sb.append(i.toString)
      sb.append(",")
    }
    sb.setLength(sb.length - 1)
    sb.append("\n")

    for(vectorString <- vectorsAsString) {
      sb.append(vectorString)
      cfile.append(vectorString)
    }

    println("Saving file to " + new Path(path, "data").toUri.toString)
    sc.parallelize(sb.toString().split("\n"), 1).saveAsTextFile(new Path(path+".csv", "data").toUri.toString)
    sc.parallelize(cfile.toString().split("\n"), 1).saveAsTextFile(new Path(path+".cs", "data").toUri.toString)
  }

显然它的工作方式类似于current implementation - 它没有。

我想获得一个word2vec模型。它适用于小文件，但如果模型变大则不适用。

Answer 1

当执行程序上的MetadataFetchFailedException无法为本地缓存中的分区找到请求的随机抽取映射输出并尝试从驱动程序MapOutputTracker远程获取它们时，抛出

MapOutputTracker。

这可能导致一些结论：

司机的记忆问题
遗嘱执行人的记忆问题
执行人员迷失

请查看日志，查找报告为“Executor lost”INFO消息的问题和/或查看Web UI的Executors页面，并查看执行程序的工作方式。

执行程序丢失的根本原因也可能是集群管理器决定杀死行为不端的执行程序（可能耗尽了超出请求的内存）。

有关更多见解，请参阅其他问题FetchFailedException or MetadataFetchFailedException when processing big data set。

如何解决＆＃34; MetadataFetchFailedException：缺少shuffle的输出位置＆＃34;？

1 个答案: