scala - 较长的RDD沿袭导致Stackoverflow

HDFS中有数千个小文件。需要处理稍微小的文件子集（又是数千个），fileList包含需要处理的文件路径的列表。

// fileList == list of filepaths in HDFS

var masterRDD: org.apache.spark.rdd.RDD[(String, String)] = sparkContext.emptyRDD

for (i <- 0 to fileList.size() - 1) {

val filePath = fileStatus.get(i)
val fileRDD = sparkContext.textFile(filePath)
val sampleRDD = fileRDD.filter(line => line.startsWith("#####")).map(line => (filePath, line))

masterRDD = masterRDD.union(sampleRDD)

}

masterRDD.first()

//一旦退出循环，由于RDD的沿袭很长，执行任何操作都会导致堆栈溢出错误

Exception in thread "main" java.lang.StackOverflowError
    at scala.runtime.AbstractFunction1.<init>(AbstractFunction1.scala:12)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.<init>(UnionRDD.scala:66)
    at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
    at scala.collection.AbstractTraversable.map(Traversable.scala:105)
    at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
    =====================================================================
    =====================================================================
    at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)

最佳答案

通常，您可以使用检查点来中断较长的血统。与此类似或多或少的应该起作用：

import org.apache.spark.rdd.RDD
import scala.reflect.ClassTag

val checkpointInterval: Int = ???

def loadAndFilter(path: String) = sc.textFile(path)
  .filter(_.startsWith("#####"))
  .map((path, _))

def mergeWithLocalCheckpoint[T: ClassTag](interval: Int)
  (acc: RDD[T], xi: (RDD[T], Int)) = {
    if(xi._2 % interval == 0 & xi._2 > 0) xi._1.union(acc).localCheckpoint
    else xi._1.union(acc)
  }

val zero: RDD[(String, String)] = sc.emptyRDD[(String, String)]
fileList.map(loadAndFilter).zipWithIndex
  .foldLeft(zero)(mergeWithLocalCheckpoint(checkpointInterval))

在这种特殊情况下，更简单的解决方案应该是使用SparkContext.union方法：

val masterRDD = sc.union(
  fileList.map(path => sc.textFile(path)
    .filter(_.startsWith("#####"))
    .map((path, _)))
)

当您查看由loop / reduce生成的DAG时，这些方法之间的区别应该很明显：



和一个union：



当然，如果文件很小，则可以将wholeTextFiles与flatMap组合在一起并一次读取所有文件：

sc.wholeTextFiles(fileList.mkString(","))
  .flatMap{case (path, text) =>
    text.split("\n").filter(_.startsWith("#####")).map((path, _))}

关于scala - 较长的RDD沿袭导致Stackoverflow，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/34461804/