hadoop编程:词频排序

1万 · 2018-10-11 13:48

本文博客链接:http://blog.csdn.net/jdh99,作者:jdh,转载请注明.

环境：

主机:Ubuntu10.04

hadoop版本:1.2.1

开发工具:gedit

说明:

在词频排序的例子上增加

教程中词频排序不能识别，；等符号，造成“soft”和“soft，”被识别为两个词，所以进行修改。

1万 · 2018-10-11 13:49

源代码:

TokenizerMapper.java:

package com.bazhangkeji.hadoop;

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 TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>

{

IntWritable one = new IntWritable(1);

Text word = new Text();

public void map(Object key, Text value, Context context) throws IOException, InterruptedException

{

StringTokenizer itr = new StringTokenizer(value.toString());

StringBuffer str_temp = new StringBuffer();

StringBuffer str_out = new StringBuffer();

int i = 0;

while (itr.hasMoreTokens())

{

str_temp.setLength(0);

str_out.setLength(0);

str_temp.append(itr.nextToken());

for (i = 0;i < str_temp.length();i++)

{

if ((str_temp.charAt(i) >= 'a' && str_temp.charAt(i) <= 'z') ||

(str_temp.charAt(i) >= 'A' && str_temp.charAt(i) <= 'Z') ||

(str_temp.charAt(i) >= '0' && str_temp.charAt(i) <= '9'))

{

str_out.append(str_temp.charAt(i));

}

word.set(str_out.toString());

context.write(word, one);

}

1万 · 2018-10-11 13:49

IntSumReducer.java：

package com.bazhangkeji.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable>
{
    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);
    }
}

1万 · 2018-10-11 13:50

WordCount.java：

package com.bazhangkeji.hadoop;
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 WordCount
{
    public static void main(String[] args) throws Exception
{
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, "wordcount");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setReducerClass(IntSumReducer.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);
    }
}

hadoop编程:词频排序

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