spark算子实现词频统计（java版和scala版）

//spark实现本地文件读取并用reduce进行聚合

package day04
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object Test04 {
  def main(args: Array[String]) {
    val conf: SparkConf =
      new SparkConf().setMaster("local").setAppName("Test04")
    val sc :SparkContext=new SparkContext(conf)
    val pRdd : RDD[Int]=sc.parallelize(Array(1,2,3,4,5))
    val textFile:RDD[String]=sc.textFile("D:\\1.txt")
    val reduceRdd:Int=pRdd.reduce(_+_)
    println(reduceRdd.toString)
    sc.stop()
  }
}

//spark实现词频统计（scala版）

package day03
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object WordCount_Scala {
  def main(args: Array[String]) {
    //local[*] 使用当前环境的默认cpu核数作为并行的线程数
    //local    使用一个线程来运行
    val conf: SparkConf =
      new SparkConf().setMaster("local").setAppName("WordCount_Scala")
    val sc: SparkContext = new SparkContext(conf)
    val file: RDD[String] = sc.textFile("D:\\1.txt")
    val flatMap: RDD[String] = file.flatMap(_.split("\\s"))
    val map: RDD[(String, Int)] = flatMap.map((_,1))
    val key = map.reduceByKey(_+_)
    val map1: RDD[(Int, String)] = key.map(x=>(x._2,x._1))
    val map2: RDD[(Int, String)] = map1.sortByKey(false) //不写或写true-->正序   false-->倒序
    val map3: RDD[(String, Int)] = map2.map(x=>(x._2,x._1))
    map3.foreach(x=>{
      println("当前的单词是     : " + x._1)
      println("单词出现的次数是 : " + x._2)
    })
  }

}

//spark实现词频统计（java版）

package day03;
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.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
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 WordCount_Java {
    public static void main(String[]args){
        SparkConf conf =
                new SparkConf().setMaster("local").setAppName("WordCount_Java");
        JavaSparkContext jsc = new JavaSparkContext(conf);
        JavaRDD<String> file = jsc.textFile("D:\\1.txt");
        JavaRDD<String> words = file.flatMap(new FlatMapFunction<String, String>() {
            public Iterator<String> call(String line) throws Exception {
                return Arrays.asList(line.split(" ")).iterator();
            }
        });
        JavaPairRDD<String, Integer> wordMap = words.mapToPair(new PairFunction<String, String, Integer>() {
            public Tuple2<String, Integer> call(String word) throws Exception {
                return new Tuple2<String, Integer>(word, 1);
            }
        });
        JavaPairRDD<String, Integer> reduceWord = wordMap.reduceByKey(new Function2<Integer, Integer, Integer>() {
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });
        JavaPairRDD<Integer, String> pairRDD = reduceWord.mapToPair(new PairFunction<Tuple2<String, Integer>,
                Integer, String>() {
            public Tuple2<Integer, String> call
                    (Tuple2<String, Integer> t2) throws Exception {
                return new Tuple2<Integer, String>(t2._2, t2._1);
            }
        });
        JavaPairRDD<Integer, String> sortPairRDD = pairRDD.sortByKey(false);
        JavaPairRDD<String, Integer> resultRdd = sortPairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
            public Tuple2<String, Integer> call
                    (Tuple2<Integer, String> t2) throws Exception {
                return new Tuple2<String, Integer>(t2._2, t2._1);
            }
        });
        resultRdd.foreach(new VoidFunction<Tuple2<String, Integer>>() {
            public void call
                    (Tuple2<String, Integer> t2) throws Exception {
                System.out.println("出现的单词是     : " + t2._1);
                System.out.println("单词出现的次数是 : " + t2._2);
            }
        });
    }
}