win10 + hadoop2.7.3 + idea 运行第一个MapReduce项目：WordCount

一、启动Hadoop

  如果还没有安装可以参考文章：Win10下安装Hadoop2.7.3
  切换到 ……\hadoop-2.7.3\sbin 下，运行命令 .\start-dfs.cmd
  没有报错一般就是正常启动，也可以在浏览器中输入 http://localhost:50070 查看Hadoop 的相关信息。

二、在IDEA中运行程序

1. 新建Maven项目
   
     GroupId 是项目唯一标识符，一般分为三段，域名.公司/组织.子项目名 ，域名一般有cn(china)，org(非营利组织)，com(商业组织)，ArtifacrId 即项目名。
   
     点击 Next ，这里一般不需要做改动。
   
     之后点击 Next，新建项目一般会询问是否自动导入包，可以选择 Enable Auto-Import。
   
2. 导入项目依赖库，在 pom.xml 中导入以下依赖，会自动进行下载导入。

	<dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-common</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-yarn-client</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-core</artifactId>
            <version>2.7.3</version>
        </dependency>
    </dependencies>

    <build>
        <finalName>${project.artifactId}</finalName>
    </build>

3. 导入项目，WordCount源代码在官网(http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html#Example:_WordCount_v1.0)有，可以直接复制。
   在 src/main/java 下新建Package WordCount，再建class ，命名 WordCount，复制代码，因为源码是中输入输出文件路径是以 args 参数给出的，如果需要直接运行可以更改 args[0]为自定义的输入文件路径、args[1]为输出文件路径。最后点击右键选择 Run ‘WordCount.main()’ 即可。
   

package WordCount;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount {

    public static class TokenizerMapper
            extends Mapper<Object, Text, Text, IntWritable>{

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context
        ) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    public static class IntSumReducer
            extends Reducer<Text,IntWritable,Text,IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Context context
        ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
//        FileInputFormat.addInputPath(job, new Path(args[0]));
//        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        FileInputFormat.addInputPath(job, new Path("F:\\data\\input\\countword.txt"));
        FileOutputFormat.setOutputPath(job, new Path("F:\\data\\output\\countword"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

4. 输入数据

Hello	World	Bye	World	Summer	Summer

5. 输出数据

Bye	1
Hello	1
Summer	2
World	2

---

参考文章：【1】第一个MapReduce程序——WordCount | 神奕的博客
     【2】Win10+hadoop+idea 运行wordcount - Fasdfg12的博客
     【3】【Hadoop】Windows 10 在Intellij IEDA本地运行Hadoop MapReduce实例 - m0_37324825的博客
     【4】Apache Hadoop 3.2.1 – MapReduce Tutorial