Spark 分组 TOPN 排序

/**
 * Created by zhangshuai on 2016/9/22.
 */
//        输入文件
//        Spark 100
//        Hadoop 65
//        Spark 99
//        Hadoop 61
//        Spark 195
//        Hadoop 60
//        Spark 98
//        Hadoop 69
//        Spark 91
//        Hadoop 64
//        Spark 89
//        Hadoop 98
//        Spark 88
//        Hadoop 99
//        Spark 68
//        Hadoop 60
//        Spark 79
//        Hadoop 97
//        Spark 69
//        Hadoop 96



//        结果输出
//        Group key :Spark
//        195
//        100
//        99
//        98
//        91
//        *********************
//        Group key :Hadoop
//        99
//        98
//        97
//        96
//        69
//        **********************

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction;

import scala.Tuple2;

import java.util.Arrays;
import java.util.Iterator;


public class TopNGroupJava {
    public static void main(String[] args) {
        // TODO Auto-generated method stub
        SparkConf conf = new SparkConf().setAppName("TopNGroupJava")
                .setMaster("local");

        JavaSparkContext sc = new JavaSparkContext(conf); //其底层实际上就是Scala的SparkContext
        JavaRDD<String> lines = sc.textFile(
                "E://topn.txt");

        JavaPairRDD<String, Integer> pairs = lines.mapToPair(new PairFunction<String, String, Integer>() {
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, Integer> call(String line)
                    throws Exception {
                // TODO Auto-generated method stub
                String[] splitedLine = line.split(" ");
                System.out.println(splitedLine[0]);

                return new Tuple2<String, Integer>(splitedLine[0],Integer.valueOf(splitedLine[1]));
            }
        });

        JavaPairRDD<String, Iterable<Integer>> groupedPairs = pairs.groupByKey();

        JavaPairRDD<String, Iterable<Integer>> top5 = groupedPairs.mapToPair(new PairFunction<Tuple2<String, Iterable<Integer>>, String, Iterable<Integer>>() {
            /**
             *
             */
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2<String, Iterable<Integer>> call(Tuple2<String, Iterable<Integer>> groupedData) throws Exception {
                Integer[] top5 = new Integer[5]; //保存top5本身
                String groupedKey = groupedData._1(); //获取分组组名
                Iterator<Integer> groupedValue = groupedData._2().iterator(); //获取每组的内容集合

                while (groupedValue.hasNext()) { //查看下一个元素，如果有继续循环
                    Integer value = groupedValue.next(); //获取当前循环的元素本身内容

                    for (int i = 0; i < 5; i++) {
                        if (top5[i] == null) {
                            top5[i] = value;

                            break;
                        } else if (value > top5[i]) {
                            for (int j = 4; j > i; j--) {
                                top5[j] = top5[j - 1];
                            }

                            top5[i] = value;

                            break;
                        }
                    }
                }

                return new Tuple2<String, Iterable<Integer>>(groupedKey,
                        Arrays.asList(top5));
            }
        });

        top5.foreach(new VoidFunction<Tuple2<String, Iterable<Integer>>>() {
            @Override
            public void call(Tuple2<String, Iterable<Integer>> topped)
                    throws Exception {
                System.out.println("Group key :" + topped._1());

                Iterator<Integer> toppedValue = topped._2().iterator();

                while (toppedValue.hasNext()) {
                    Integer value = toppedValue.next();
                    System.out.println(value);
                }

                System.out.println("**********************");
            }
        });
    }
}

import org.apache.spark.{SparkContext, SparkConf}

/**
 * Created by zhangshuai on 2016/9/22.
 */
//输入文件
//Spark,100
//Hadoop,62
//Flink,77
//Kafka,91
//Hadoop,93
//Spark,78
//Hadoop,69
//Spark,98
//Hadoop,62
//Spark,99
//Hadoop,61
//Spark,70
//Hadoop,75
//Spark,88
//Hadoop,68
//Spark,90
//Hadoop,61



//结果输出
//Flink:
//77
//Hadoop:
//61
//61
//62
//62
//68
//Kafka:
//91
//Spark:
//70
//78
//88
//90
//98
object TopNGroupScala {
  def main(args: Array[String]) {
     val conf=new SparkConf().setAppName("TopNGroupScala").setMaster("local")

     val sc=new SparkContext(conf)
     sc.setLogLevel("WARN")
     val lines=sc.textFile("E://topn.txt",1)

     val pairs=lines.map{(line =>(line.split(",")(0),line.split(",")(1).toInt))}

     val grouped=pairs.groupByKey()

     val groupedTop5=grouped.map(grouped =>
      {
        (grouped._1,grouped._2.toList.sortWith(_<_).take(5))
      }
     )
     val groupedKeySorted=groupedTop5.sortByKey()

     groupedKeySorted.collect().foreach(pair =>
     {
       println(pair._1+":")
       pair._2.foreach{println}
     }
     )
    sc.stop()

  }
}