hadoop 实现简单的wordcount实例

前置条件：
在hadoop官网下载某个版本的zip文件，这里下载的版本是2.7.3，将其解压刀你的电脑的某个目录中，这里为：D:\dev\hadoop-2.7.3
下载地址：http://apache.fayea.com/hadoop/common/hadoop-2.7.3/

src的是文件源码，有需要的可以下载下来研究~
配置环境变量：
HADOOP_HOME D:\dev\hadoop-2.7.3

1.使用idea新建一个maven项目
2.修改maven项目中pom文件，加入如下依赖

    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>

    <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-mapreduce-client-core</artifactId>
      <version>2.7.3</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</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-client</artifactId>
      <version>2.7.3</version>
    </dependency>

3.在java文件新建一个包 com.hadoop.wordcount 名字可以自定义
在包内新建一个类 wordCount
内容如下：

package com.hadoop.wordcount;

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;

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

/**
 * WordCount
 *
 * @author: wychen
 * @time: 2017/3/20 20:25
 */
public class WordCount {

    static class MyMapper extends Mapper<Object, Text, Text, IntWritable> {

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

        @Override
        protected void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            //分割字符串
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                //排除字母少于5个的
                String tmp = itr.nextToken();
                if (tmp.length() < 5) {
                    continue;
                }
                word.set(tmp);
                context.write(word, one);
            }
        }


    }

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

        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,
                              Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                //将map的结果方法，乘以2
                sum += val.get() + 1;

            }
            result.set(sum);

            keyEx.set("输出：" + key.toString());
            context.write(keyEx, result);
        }
    }

    public static void main(String[] args) throws Exception {

        //配置信息
        Configuration conf = new Configuration();

        //job名称
        Job job = Job.getInstance(conf, "mywordcount");

        job.setJarByClass(WordCount.class);
        job.setMapperClass(MyMapper.class);

        job.setReducerClass(MyReduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //输入 输出path
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        //结束
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }


}

4.resources 文件中新建日志配置文件 log4j.properties

log4j.rootLogger=DEBUG, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%c{1} - %m%n
log4j.logger.java.sql.PreparedStatement=DEBUG

接下来就可以直接WordCount类中运行main函数了
首先配置运行前的参数

接下来直接在WordCount类中右击鼠标，点击运行即可，可在控制台查看运行过程中输出的结果，以及你填写的文件输出路径的文件中结果
笔者的输出结果如下图所示：

好了，一个简单的MapReduce下的单词统计实例就完成了~