MapReduce编程初体验（idea）：统计一个文档里的单词个数

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
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    <modelVersion>4.0.0</modelVersion>

    <groupId>com.czxy</groupId>
    <artifactId>test</artifactId>
    <version>1.0-SNAPSHOT</version>


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            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
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            <version>2.6.0-cdh5.14.0</version>
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        <dependency>
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            <version>2.6.0-cdh5.14.0</version>
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package com.czxy.test01;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;
public class WordCountMap extends Mapper<LongWritable, Text,Text,LongWritable> {
	//第一个参数 LongWritable 是map的输入的 key（数据类型固定，偏移量）
	//第二个参数 Text是map的输入的 value（数据类型固定，输入的文档）
	//第三个参数 Text是map的输出的 key（数据类型可变，根据需求而变）
	//第四个参数 LongWritable 是map的输出的 value（数据类型可变，根据需求而变）

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //key 是行首字母的偏移量
        //      偏移量：每个字符移动到当前文档的最前面需要移动的字符个数（空格和回车也要算）
        //value 代码中一行数据

        //1.将 Text 类型的 value 转化为 string
        String data = value.toString();
        //2.使用“ ”对数据切分
        String[] strings = data.split(" ");
        //3.遍历每个单词，进行输出（一个单词输出一次）
        for (String string : strings) {
            //输出数据
            //context 上下文对象
            //根据业务需求进行切分，逐一输出
            context.write(new Text(string),new LongWritable(1));
        }
    }
}

package com.czxy.test01;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
public class WordCountReduce extends Reducer<Text,LongWritable,Text,LongWritable> {
	//第一个参数 Text是reduce的输入的 key（数据类型与map的输出key一致）
	//第二个参数 LongWritable 是reduce的输入的 value（数据类型与map的输出value一致）
	//第三个参数 Text是reduce的输出的 key（数据类型reduce的输入的 key一致）
	//第四个参数 LongWritable 是reduce的输出的 value（数据类型reduce的输入的 value一致）
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        //key 表示去重后的单词
        //value 表示标记的1 （好多 1，key出现几次就有几个1）

        int sum =0;

        //求和： 遍历values ，逐一求和
        for (LongWritable value : values) {
            sum += value.get();
        }
        //将结果输出
        context.write(key,new LongWritable(sum));
    }
}

package com.czxy.test01;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class WordCountDriver extends Configured implements Tool {
    /**
     * 将自己的 map  Reduce 代码添加到框架中
     */
    @Override
    public int run(String[] strings) throws Exception {
        //1.实例一个 Job
        Job job = Job.getInstance(new Configuration(), "wordCount12");

        //2.设置读取数据的class
        job.setInputFormatClass(TextInputFormat.class);
        //  设置读取数据的路径
        TextInputFormat.addInputPath(job,new Path("D:\\d12-19\\idea\\work\\wordcount.txt"));

        //3.设置map
        job.setMapperClass(WordCountMap.class);
        //  设置map输出的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //4.设置reduce
        job.setReducerClass(WordCountReduce.class);
        //  设置reduce的输出的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        //5.设置输出数据的class
        job.setOutputFormatClass(TextOutputFormat.class);
        //  设置输出数据的路径
        TextOutputFormat.setOutputPath(job,new Path("E://wordCount//output"));
        //6.等待代码执行（返回状态码）

        return job.waitForCompletion(true)?0:1;
    }

    public static void main(String[] args) throws Exception {
        //调用执行
        int run = ToolRunner.run(new WordCountDriver(), args);
    }
}

 Exception in thread "main" org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory E:/wordCount/output already exists