Hadoop_英文单词统计

一、把Hadoop的插件文件放到Eclipse路径的plugin文件夹内

重启Eclipse，Window -> Preferences ->Hadoop Map/Reduce 选择解压的过后的hadoop程序路径

项目视图里就会出现DFS分布式文件系统的选项。

 

二、创建与服务节点的连接

右键图中红色框内新建一个与Master节点的连接

如图填写好name，ip，port就可以在DFS中访问该节点的信息

 

三、new一个新项目就会出现Map/Reduce选项

项目文件结构如下：

 

四、编写Mapper类

右键包名new -> Mapper

package org.znufe.cnwc;

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

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

public class CNWordMapper extends Mapper<Object, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    public void map(Object ikey, Text value, Context context)
            throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while(itr.hasMoreTokens()){
            word.set(itr.nextToken());
            context.write(word, one);            
        }        
    }
}

 

五、编写Reducer类

右键包名new -> Reducer

package org.znufe.cnwc;

import java.io.IOException;

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

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

}

 

六、编写Driver驱动类

右键包名new -> MapReduce Driver

package org.znufe.cnwc;

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 org.apache.hadoop.util.GenericOptionsParser;

public class CNWordMain {

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        /**
         * 这里必须有输入和输出
         * */
        if(otherArgs.length != 2){
            System.err.println("Usage: wordcount <in> <out>");
            System.exit(2);
        }
        Job job = Job.getInstance(conf, "CN Word Count");
        job.setJarByClass(org.znufe.cnwc.CNWordMain.class);
        // TODO: specify a mapper
        job.setMapperClass(org.znufe.cnwc.CNWordMapper.class);
        // TODO: specify a reducer
        job.setReducerClass(org.znufe.cnwc.CNWordReducer.class);

        // TODO: specify output types
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // TODO: specify input and output DIRECTORIES (not files)
        FileInputFormat.setInputPaths(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

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

 

七、把项目打包成jar文件并上传

右键项目名 -> Export，取消勾选classpath与project，填好路径与名字

把项目jar包上传到Master节点Hadoop主目录下（我的是/home/hadoop/hadoop-2.5.2)

 

八、在Master节点创建文档进行词频统计测试

1. 在hadoop-2.5.2文件夹中创建两个文件

test.txt

Hello World!
Hello Hadoop!

test1.txt

Hello What Ghost!
4S is Super Stupid Suspension System.

然后通过命令把两个文件上传到文件系统中

bin/hdfs dfs -copyFromLocal /home/hadoop/hadoop-2.5.2/test.txt /testtemp

bin/hdfs dfs -copyFromLocal /home/hadoop/hadoop-2.5.2/test1.txt /testtemp

此时可以通过bin/hdfs dfs -ls /testtemp这个命令或者直接刷新Eclipse的文件列表查看是否上传成功

 

2. 运行程序（我打包的程序是wordcount.jar）

bin/hadoop jar wordcount.jar org.znufe.cnwc.CNWordMain /testtemp/ /outputwordcount_01       //注意第二个路径必须是不存在的

如果成功就会看到以下结果

4. 在Eclipse中就可以查看到统计结果

 

一些基本的Hadoop命令

hadoop fs -copyFromLocal

hadoop fs -cooyToLocal