火花列明智字数

Question

我们正试图在spark中生成数据集的逐列统计数据。除了使用统计库中的summary函数之外。我们使用以下程序：

>

我们确定具有字符串值的列

为整个数据集生成键值对，使用列号作为键，使用列的值作为值

生成新的格式映射

（K，V）-

然后我们使用reduceByKey来找到所有列中所有唯一值的总和。我们缓存这个输出以减少进一步的计算时间。

在下一步中，我们使用for循环遍历列，以查找所有列的统计信息。

我们试图通过再次使用map reduce方法来减少for循环，但我们无法找到某种方法来实现它。这样做将允许我们在一次执行中为所有列生成列统计信息。for循环方法按顺序运行，速度非常慢。

代码：

//drops the header

    def dropHeader(data: RDD[String]): RDD[String] = {
         data.mapPartitionsWithIndex((idx, lines) => {
           if (idx == 0) {
             lines.drop(1)
           }
           lines
         })
       }

    def retAtrTuple(x: String) = {
       val newX = x.split(",")
       for (h <- 0 until newX.length) 
          yield (h,newX(h))
    }



    val line = sc.textFile("hdfs://.../myfile.csv")

    val withoutHeader: RDD[String] = dropHeader(line)

    val kvPairs = withoutHeader.flatMap(retAtrTuple) //generates a key-value pair where key is the column number and value is column's value


    var bool_numeric_col = kvPairs.map{case (x,y) => (x,isNumeric(y))}.reduceByKey(_&&_).sortByKey()    //this contains column indexes as key and boolean as value (true for numeric and false for string type)

    var str_cols = bool_numeric_col.filter{case (x,y) => y == false}.map{case (x,y) => x}
    var num_cols = bool_numeric_col.filter{case (x,y) => y == true}.map{case (x,y) => x}

    var str_col = str_cols.toArray   //array consisting the string col
    var num_col = num_cols.toArray   //array consisting numeric col


    val colCount = kvPairs.map((_,1)).reduceByKey(_+_)
    val e1 = colCount.map{case ((x,y),z) => (x,(y,z))}
    var numPairs = e1.filter{case (x,(y,z)) => str_col.contains(x) }

    //running for loops which needs to be parallelized/optimized as it sequentially operates on each column. Idea is to find the top10, bottom10 and number of distinct elements column wise
    for(i <- str_col){
       var total = numPairs.filter{case (x,(y,z)) => x==i}.sortBy(_._2._2)
       var leastOnes = total.take(10)
       println("leastOnes for Col" + i)
       leastOnes.foreach(println)
       var maxOnes = total.sortBy(-_._2._2).take(10)
       println("maxOnes for Col" + i)
       maxOnes.foreach(println)
       println("distinct for Col" + i + " is " + total.count)
    }

Answer 1

感谢@Daniel Darabos的回答。但是有一些错误。

>

地图和收藏的混合使用。易变的。地图

（i： Int）=

下面是修改后的代码：

//import org.apache.spark.util.BoundedPriorityQueue // Pretend it's not private. copy to your own folder and import it
import org.apache.log4j.{Level, Logger}
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}


object BoundedPriorityQueueTest {

  //  https://stackoverflow.com/questions/28166190/spark-column-wise-word-count
  def top(n: Int, rdd: RDD[(Int, String)]): Map[Int, Iterable[String]] = {
    // A heap that only keeps the top N values, so it has bounded size.
    type Heap = BoundedPriorityQueue[(Long, String)]
    // Get the word counts.
    val counts: RDD[((Int, String), Long)] =
    rdd.map(_ -> 1L).reduceByKey(_ + _)
    // In each partition create a column -> heap map.
    val perPartition: RDD[collection.mutable.Map[Int, Heap]] =
    counts.mapPartitions { items =>
      val heaps =
        collection.mutable.Map[Int, Heap]() // .withDefault((i: Int) => new Heap(n))
      for (((k, v), count) <- items) {
        println("\n---")
        println("before add " + ((k, v), count) + ", the map is: ")
        println(heaps)
        if (!heaps.contains(k)) {
          println("not contains key " + k)
          heaps(k) = new Heap(n)
          println(heaps)
        }
        heaps(k) += count -> v
        println("after add " + ((k, v), count) + ", the map is: ")
        println(heaps)

      }
      println(heaps)
      Iterator.single(heaps)
    }
    // Merge the per-partition heap maps into one.
    val merged: collection.mutable.Map[Int, Heap] =
    perPartition.reduce { (heaps1, heaps2) =>
      val heaps =
        collection.mutable.Map[Int, Heap]() //.withDefault((i: Int) => new Heap(n))
      println(heaps)
      for ((k, heap) <- heaps1.toSeq ++ heaps2.toSeq) {
        for (cv <- heap) {
          heaps(k) += cv
        }
      }
      heaps
    }
    // Discard counts, return just the top strings.
    merged.mapValues(_.map { case (count, value) => value }).toMap
  }

  def main(args: Array[String]): Unit = {
    Logger.getRootLogger().setLevel(Level.FATAL) //http://stackoverflow.com/questions/27781187/how-to-stop-messages-displaying-on-spark-console
    val conf = new SparkConf().setAppName("word count").setMaster("local[1]")
    val sc = new SparkContext(conf)
    sc.setLogLevel("WARN") //http://stackoverflow.com/questions/27781187/how-to-stop-messages-displaying-on-spark-console


    val words = sc.parallelize(List((1, "s11"), (1, "s11"), (1, "s12"), (1, "s13"), (2, "s21"), (2, "s22"), (2, "s22"), (2, "s23")))
    println("# words：" + words.count())

    val result = top(1, words)

    println("\n--result:")
    println(result)
    sc.stop()

    print("DONE")
  }

}

Answer 2

让我把你的问题简化一点。（实际上很多。）我们有一个RDD[（Int，String）]，我们想为每个Int（都在0-100范围内）找到前10个最常见的Strings。

与示例中的排序不同，使用Spark内置的RDD更有效。顶部（n）方法。它的运行时间与数据的大小成线性关系，与排序相比，需要移动的数据要少得多。

在RDD中考虑<代码> Tope<代码>的实现。斯卡拉。您也希望这样做，但每个Int键都有一个优先级队列（堆）。代码变得相当复杂：

import org.apache.spark.util.BoundedPriorityQueue // Pretend it's not private.

def top(n: Int, rdd: RDD[(Int, String)]): Map[Int, Iterable[String]] = {
  // A heap that only keeps the top N values, so it has bounded size.
  type Heap = BoundedPriorityQueue[(Long, String)]
  // Get the word counts.
  val counts: RDD[[(Int, String), Long)] =
    rdd.map(_ -> 1L).reduceByKey(_ + _)
  // In each partition create a column -> heap map.
  val perPartition: RDD[Map[Int, Heap]] =
    counts.mapPartitions { items =>
      val heaps =
        collection.mutable.Map[Int, Heap].withDefault(i => new Heap(n))
      for (((k, v), count) <- items) {
        heaps(k) += count -> v
      }
      Iterator.single(heaps)
    }
  // Merge the per-partition heap maps into one.
  val merged: Map[Int, Heap] =
    perPartition.reduce { (heaps1, heaps2) =>
      val heaps =
        collection.mutable.Map[Int, Heap].withDefault(i => new Heap(n))
      for ((k, heap) <- heaps1.toSeq ++ heaps2.toSeq) {
        for (cv <- heap) {
          heaps(k) += cv
        }
      }
      heaps
    }
  // Discard counts, return just the top strings.
  merged.mapValues(_.map { case(count, value) => value })
}

这是有效的，但会带来痛苦，因为我们需要同时处理多个列。如果每列有一个RDD，只需调用RDD就可以了。顶部（10）。

不幸的是，将RDD分割成N个较小的RDD的幼稚方法没有通过N次：

def split(together: RDD[(Int, String)], columns: Int): Seq[RDD[String]] = {
  together.cache // We will make N passes over this RDD.
  (0 until columns).map {
    i => together.filter { case (key, value) => key == i }.values
  }
}

一个更有效的解决方案可能是按键将数据写入单独的文件，然后将其加载回单独的RDD。这在按键Spark-one Spark作业写入多个输出中讨论。