wordCount就是對一大堆單詞進行個數統計，然後排序。從網上找篇英文文章放到本地文件。

  pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.fei</groupId>
  <artifactId>word-count</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  
  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>
     <dependency>
	  <groupId>org.apache.spark</groupId>
	  <artifactId>spark-core_2.10</artifactId>
	  <version>1.3.0</version>
	</dependency>
	
	
  </dependencies>
  <build> 
    <plugins> 
        <plugin> 
            <groupId>org.apache.maven.plugins</groupId> 
            <artifactId>maven-compiler-plugin</artifactId> 
            <version>2.0.2</version> 
            <configuration> 
                <source>1.8</source> 
                <target>1.8</target> 
            </configuration> 
        </plugin> 
    </plugins> 
</build> 
</project>
 

package com.fei;

import java.util.Arrays;
import java.util.List;

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 scala.Tuple2;

/**
 * 單詞統計，並按降序排序，輸出前10個單詞及個數
 * @author Jfei
 *
 */
public class WordCount {

	public static void main(String[] args) {
		//1.本地模式，建立spark配置及上下文
		SparkConf conf = new SparkConf().setAppName("wordCount").setMaster("local");
		JavaSparkContext sc = new JavaSparkContext(conf);
		
		//2.讀取本地檔案,並建立RDD
		JavaRDD<String> linesRDD = sc.textFile("e:\\words.txt");
		//3.每個單詞由空格隔開,將每行的linesRDD拆分為每個單詞的RDD
		JavaRDD<String> wordsRDD = linesRDD.flatMap(s  -> Arrays.asList(s.split("\\s")));
		//相當於 ==>
		/*JavaRDD<String> wordsRDD = linesRDD.flatMap(new FlatMapFunction<String, String>(){
			private static final long serialVersionUID = 1L;
			@Override
			public Iterable<String> call(String line) throws Exception {
				return Arrays.asList(line.split(" "));
			}
		});*/
		//4.將每個單詞轉為key-value的RDD，並給每個單詞計數為1
		JavaPairRDD<String,Integer> wordsPairRDD = wordsRDD.mapToPair(s -> new Tuple2<String,Integer>(s, 1));
		//相當於 ==>
		/*JavaPairRDD<String,Integer> wordsPairRDD = wordsRDD.mapToPair(new PairFunction<String, String, Integer>() {
			private static final long serialVersionUID = 1L;
			@Override
			public Tuple2<String, Integer> call(String word) throws Exception {
				return new Tuple2<String,Integer>(word,1);
			}
		});*/
		
		//5.計算每個單詞出現的次數
		 JavaPairRDD<String,Integer> wordsCountRDD = wordsPairRDD.reduceByKey((a,b) -> a+b);
		//相當於 ==>
		/*JavaPairRDD<String,Integer> wordsCountRDD = wordsPairRDD.reduceByKey(new Function2<Integer, Integer, Integer>() {
			@Override
			public Integer call(Integer v1, Integer v2) throws Exception {
				return v1 + v2;
			}
		});*/
		 
		 //6.因為只能對key進行排序，所以需要將wordsCountRDD進行key-value倒置，返回新的RDD
		 JavaPairRDD<Integer,String> wordsCountRDD2 = wordsCountRDD.mapToPair(s -> new Tuple2<Integer,String>(s._2, s._1));
		 //相當於 ==>
           /*JavaPairRDD<Integer,String> wordsCountRDD2 = wordsCountRDD.mapToPair(new PairFunction<Tuple2<String,Integer>, Integer, String>() {
			private static final long serialVersionUID = 1L;
			@Override
			public Tuple2<Integer, String> call(Tuple2<String, Integer> t) throws Exception {
				return new Tuple2<Integer,String>(t._2,t._1);
			}
		});*/
         
		 //7.對wordsCountRDD2進行排序,降序desc
		 JavaPairRDD<Integer,String> wordsCountRDD3 = wordsCountRDD2.sortByKey(false);
		 
		 //8.只取前10個
		 List<Tuple2<Integer, String>>  result = wordsCountRDD3.take(10);
		 
		 //9.列印
		 result.forEach(t -> System.out.println(t._2 + "   " + t._1));
		 
		 
		 sc.close();
	 }
}