[hadoop]简单的MapReduce项目，计算文件中单词出现的次数（五）

计算文件中单词出现的次数，试题如下图

1、创建读取单词的文件tast，内容如下：

hadoop core map reduce hiv hbase Hbase
pig hadoop mapreduce MapReduce Hadoop Hbase
spark

2、流程图如下：

根据上图得知，计算流程中Mapping和Reducing是需要自己编写功能，其他交给Map/Reduce完成的

那么，我们首先编写Mapping步骤的代码，

新建WcMapper.java

package com.all58.mr;

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 WcMapper extends Mapper{
	
	private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    
    
	/**
	 * 每次调用map方法会传入split中一行数据；
	 * key：该行数据所在文件中的位置下标
	 * value：该行数据
	 */
	@Override
	protected void map(LongWritable key, Text value, Context context)
			throws IOException, InterruptedException {
		
		String line = value.toString();
		StringTokenizer itr = new StringTokenizer(line);
		while (itr.hasMoreTokens()) {
			word.set(itr.nextToken());
			context.write(word, one);//map的输出
		}
		
	}
}

新建WcReduce.java

package com.all58.mr;

import java.io.IOException;

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

public class WcReducer extends Reducer {
	
	private IntWritable result = new IntWritable();
	
	
	@Override
	protected void reduce(Text key, Iterable iter,
			Context context) throws IOException, InterruptedException {
		
		int sum = 0;
		for (IntWritable value : iter) {
			sum += value.get();
		}
		
		result.set(sum);
		context.write(key, result);
		
	}
}

到此，计算程序全部完成，下面编写Job执行程序

新建JobRun.java

package com.all58.mr;

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;

public class JobRun {
	
	public static void main(String[] args) {
		Configuration conf = new Configuration();
		conf.set("mapred.job.tracker", "node1:9001");
		
		try {
			Job job = new Job(conf);
			job.setJarByClass(JobRun.class);
			job.setMapperClass(WcMapper.class);
			job.setReducerClass(WcReducer.class);
			job.setMapOutputKeyClass(Text.class);
			job.setMapOutputValueClass(IntWritable.class);
			
			//job.setNumReduceTasks(1);//设置reduce任务的个数
			
			//mapreduce输入数据所在目录或文件
			FileInputFormat.addInputPath(job, new Path("/opt/hadoop-1.2/mapred/xiaoming"));
			//mapreduce执行之后的输出数据的目录
		    FileOutputFormat.setOutputPath(job, new Path("/opt/hadoop-1.2/mapred/xiaoming/output"));
		    System.exit(job.waitForCompletion(true) ? 0 : 1);
		    
		} catch (Exception e) {
			e.printStackTrace();
		}
		
	}
	
}

运行

1、eclipse导出jar包 wc.jar，使用scp上传至node1服务器

2、进入node1服务器~/hadoop-1.2.1/bin，执行命令

./hadoop jar ~/wc.jar com.all58.mr.JobRun

执行完毕，如下图

打开eclipse，查看结果

part-r-00000的内容：

Hadoop	1
Hbase	2
MapReduce	1
core	1
hadoop	1
hbase	1
hiv	1
map	1
mapreduce	1
pig	1
reduce	1
spark	1
hadoop	1