MapReduce程序——WordCount（Windows_Eclipse + Ubuntu14.04_Hadoop2.9.0）

Posted 2020-10-26 verygoodverygood

本文主要参考《Hadoop应用开发技术详解（作者：刘刚）》

一、工作环境

Windows7： Eclipse + JDK1.8.0

Ubuntu14.04：Hadoop2.9.0

二、准备工作——导入JAR包

1. 建一个Hadoop专用的工作空间

2. 在工作空间的目录下建一个专门用来存放开发MapReduce程序所需的Hadoop依赖的JAR包的文件夹

所需的JAR包在Ubuntu中$HADOOP_HOME/share/hadoop下，将JAR包复制到刚刚建好的文件夹中

需要的JAR包如下，可能有部分重复：

$HADOOP_HOME/share/hadoop/common

$HADOOP_HOME/share/hadoop/hdfs

$HADOOP_HOME/share/hadoop/mapreduce

$HADOOP_HOME/share/hadoop/tools/lib

$HADOOP_HOME/share/hadoop/yarn

3. 新建用户库

Windows → Preference → Java → Build Path → User Libraries → New...

看到如下界面：

点击OK后看到如下界面：

点击Add External JARs... → 在刚刚建好的文件夹中选中所有JAR包 → 打开 → OK

用户库创建成功！

三、创建一个Java工程

File → New → Java Project

除了红框的内容，其他选项默认

右击项目名 → Build Path → Add Libraries... → User Library → 选中建好的用户库

 

四、MapReduce代码的实现

1. WordMapper类

package wordCount;

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

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

public class WordMapper 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);
        }
    }
}

2. WordReducer类

package wordCount;

import java.io.IOException;

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

public class WordReducer 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);
    }
}

3. WordMain驱动类

package 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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordMain {

    public static void main(String[] args) throws Exception {
        // TODO Auto-generated method stub
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage: wordCount <in> <out>");
            System.exit(2);
        }
        Job job = new Job(conf, "word count");
        job.setJarByClass(WordMain.class);
        job.setMapperClass(WordMapper.class);
        job.setCombinerClass(WordReducer.class);
        job.setReducerClass(WordReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}

五、打包成JAR文件

右击项目名 → Export → Java → JAR file

看到如下界面：

除了红框的内容，其他选项默认

点击Finish

JAR文件生成成功！

六、部署和运行

1. 把刚刚生成的JAR文件发送到Hadoop集群的Master节点的$HADOOP_HOME下面

2. 在Master节点的$HADOOP_HOME下面创建两个待统计词频的文件，file1.txt和file2.txt

file1.txt

Hello, I love coding
Are you OK?
Hello, I love hadoop
Are you OK?

file2.txt

Hello I love coding
Are you OK ?
Hello I love hadoop
Are you OK ?

3. 上传文件到HDFS系统中

$ hdfs dfs -put ./file* input

查看是否上传成功

$ hdfs dfs -ls input

4. 运行程序

$ hdfs dfs -rm -r output #如果HDFS系统中存在output目录
$ hadoop jar wordCount.jar wordCount.WordMain input/file* output

5. 查看运行结果

$ hdfs dfs -cat output/*

 

 

以上