在hadoop上进行编写mapreduce程序，统计关键词在text出现次数

标签：mapreduce程序统计关键词数量   mapreduce处理过程   hadoop统计word数量   

如代码：

package com.clq.hadoop2;

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 MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
	final Text key2 = new Text();
	// value2 表示单词在该行中的出现次数
	final IntWritable value2 = new IntWritable(1);
	// key 表示文本行的起始位置
	// value 表示文本行
	protected void map(LongWritable key, Text value, Context context)
			throws java.io.IOException, InterruptedException {
		final String[] splited = value.toString().split(",");
		for (String word : splited) {
			key2.set(word);
			// 把key2、value2写入到context中
			context.write(key2, value2);
		}
	}
}

package com.clq.hadoop2;

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

public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
	// value3表示单词出现的总次数
	final IntWritable value3 = new IntWritable(0);
	/**
	 * key 表示单词 values 表示map方法输出的1的集合 context 上下文对象
	 */
	protected void reduce(Text key, java.lang.Iterable<IntWritable> values,
			Context context) throws java.io.IOException, InterruptedException {
		int sum = 0;
		for (IntWritable count : values) {
			sum += count.get();
		}
		// 执行到这里，sum表示该单词出现的总次数
		// key3表示单词，是最后输出的key
		final Text key3 = key;
		// value3表示单词出现的总次数，是最后输出的value
		value3.set(sum);
		context.write(key3, value3);
	}
}

package com.clq.hadoop2;

import java.io.IOException;

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.mapred.lib.HashPartitioner;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MapperReducer {

	public static void main(String[] args) throws IOException,
			InterruptedException, ClassNotFoundException {
	        //指定输入和输出路径
		final String INPUT_PATH = "hdfs://ubuntu:9000/Input";
		final String OUTPUT_PATH = "hdfs://ubuntu:9000/output";
		//创建一个job对象封装运行时所需要的信息
		final Job job = new Job(new Configuration(),"MapperReducer");
		//打成jar执行
		job.setJarByClass(MapperReducer.class);
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
		//指定自己自定义的mapper类
		job.setMapperClass(MyMapper.class);
		//指定运行mapper类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);
		//指定自己定义的reducer类
		job.setReducerClass(MyReducer.class);
		//指定reducer的key和value类型
		job.setInputFormatClass(TextInputFormat.class);
		job.setOutputFormatClass(TextOutputFormat.class);
		job.waitForCompletion(true);
		
		 
	}
}

在hadoop上进行编写mapreduce程序，统计关键词在text出现次数

标签：mapreduce程序统计关键词数量   mapreduce处理过程   hadoop统计word数量   

原文地址：http://blog.csdn.net/u012516914/article/details/38062949