package com.bean.spark.wordcount;

 import java.util.Arrays;

 import java.util.Iterator;

 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;

 public class WordCount {

 public static void main(String[] args) {

 //创建SparkConf对象，设置Spark应用程序的配置信息

 SparkConf conf = new SparkConf();

 conf.setMaster("local");

 conf.setAppName("wordcount");

 //创建SparkContext对象，Java开发使用JavaSparkContext；Scala开发使用SparkContext

 //SparkContext负责连接Spark集群，创建RDD、累积量和广播量等

 JavaSparkContext sc = new JavaSparkContext(conf);

 //sc中提供了textFile方法是SparkContext中定义的,用来读取HDFS上的

 //文本文件、集群中节点的本地文本文件或任何支持Hadoop的文件系统上的文本文件，它的返回值是JavaRDD[String]，是文本文件每一行

 JavaRDD<String> lines = sc.textFile("D:/tools/data/wordcount/wordcount.txt");

 //将每一行文本内容拆分为多个单词

 //lines调用flatMap这个transformation算子（参数类型是FlatMapFunction接口实现类）返回每一行的每个单词

 JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

 private static final long serialVersionUID = 1L;

 @Override

 public Iterator<String> call(String s) throws Exception {

 // TODO Auto-generated method stub

 return Arrays.asList(s.split(" ")).iterator();

 }

 });

 //将每个单词的初始数量都标记为1个

 //words调用mapToPair这个transformation算子（参数类型是PairFunction接口实现类，

 //PairFunction<String, String, Integer>的三个参数是<输入单词, Tuple2的key, Tuple2的value>），

 //返回一个新的RDD，即JavaPairRDD

 JavaPairRDD<String, Integer> word = words.mapToPair(new PairFunction<String, String, Integer>() {

 private static final long serialVersionUID = 1L;

 @Override

 public Tuple2<String, Integer> call(String s) throws Exception {

 // TODO Auto-generated method stub

 return new Tuple2<String, Integer>(s, 1);

 }

 });

 //计算每个相同单词出现的次数

 //pairs调用reduceByKey这个transformation算子（参数是Function2接口实现类）对每个key的value进行reduce操作，

 //返回一个JavaPairRDD，这个JavaPairRDD中的每一个Tuple的key是单词、value则是相同单词次数的和

 JavaPairRDD<String, Integer> counts = word.reduceByKey(new Function2<Integer, Integer, Integer>() {

 private static final long serialVersionUID = 1L;

 @Override

 public Integer call(Integer s1, Integer s2) throws Exception {

 // TODO Auto-generated method stub

 return s1 + s2;

 }

 });

 counts.foreach(new VoidFunction<Tuple2<String,Integer>>() {

 private static final long serialVersionUID = 1L;

 @Override

 public void call(Tuple2<String, Integer> wordcount) throws Exception {

 // TODO Auto-generated method stub

 System.out.println(wordcount._1+" : "+wordcount._2);

 }

 });

 //将计算结果文件输出到文件系统

 /*

  * HDFS

  * 新版的API

  * org.apache.hadoop.mapreduce.lib.output.TextOutputFormat

  * counts.saveAsNewAPIHadoopFile("hdfs://master:9000/data/wordcount/output", Text.class, IntWritable.class, TextOutputFormat.class, new Configuration());

  * 使用默认TextOutputFile写入到HDFS(注意写入HDFS权限，如无权限则执行：hdfs dfs -chmod -R 777 /data/wordCount/output)

          * wordCount.saveAsTextFile("hdfs://soy1:9000/data/wordCount/output");

          *

  *

  * */

 counts.saveAsTextFile("D:/tools/data/wordcount/output");

 //关闭SparkContext容器，结束本次作业

 sc.close();

 }

 }

 #-*- coding:utf-8-*-

 from __future__ import print_function

 from operator import add

 import os

 from pyspark.context import SparkContext

 '''

 wordcount

 '''

 if __name__ == "__main__":

     os.environ["HADOOP_HOME"] = "D:/tools/spark-2.0.0-bin-hadoop2.6"

     sc = SparkContext()

     lines = sc.textFile("file:///D:/tools/data/wordcount/wordcount.txt").map(lambda r: r[0:])

     counts = lines.flatMap(lambda x: x.split(' ')) \

                   .map(lambda x: (x, 1)) \

                   .reduceByKey(add)

     output = counts.collect()

     for (word, count) in output:

         print("%s: %i" % (word, count))