【Data Algorithms_Recipes for Scaling up with Hadoop and Spark】Chapter3 Top 10 NonUniqueList

：scala版

package com.bbw5.dataalgorithms.sparkimport org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport java.util.PriorityQueue/** * Assumption: for all input (K, V), K's are non-unique. * This class implements Top-N design pattern for N > 0. * The main assumption is that for all input (K, V)'s, K's * are non-unique. It means that you will find entries like * (A, 2), ..., (A, 5),... * * This is a general top-N algorithm which will work unique * and non-unique keys. * * This class may be used to find bottom-N as well (by * just keeping N-smallest elements in the set. * *  Top-10 Design Pattern: “Top Ten” Structure * *  1. map(input) => (K, V) * *  2. reduce(K, List<V1, V2, ..., Vn>) => (K, V), *                where V = V1+V2+...+Vn *     now all K's are unique * *  3. Find Top-N using the following high-level Spark API: *     java.util.List<T> takeOrdered(int N, java.util.Comparator<T> comp) *     Returns the first N elements from this RDD as defined by the specified *     Comparator[T] and maintains the order. * cat id,cat name,cat weight * 1,cat1,13 * 2,cat2,10 * 3,cat3,14 * 4,cat4,13 * 5,cat5,20 * 6,cat6,24 * 7,cat7,13 * 8,cat8,10 * 9,cat9,24 * 10,cat10,13 * 11,cat11,30 * 12,cat12,14 * @author babaiw5 * */object SparkTop10UsingTakeOrdered {  def main(args: Array[String]) {    val sparkConf = new SparkConf().setAppName("SparkTop10UsingTakeOrdered")    val sc = new SparkContext(sparkConf)    val filename = "G:/temp/data/top1.txt"    val textFile = sc.textFile(filename)    val topN = 5    val topNRDD = textFile.map(_.split(",")).map(d =>      ((d(0), d(1)), d(2).toInt)).reduceByKey((a, b) => a + b).      takeOrdered(topN)(Ordering.by[((String, String), Int), Int](-_._2))          topNRDD.foreach(println)      }}