Hadoop实战任务-----编写MapReduce进行数据清洗

一个英文书籍包含成千上万个单词或者短语,在大量的单词中,找出相同字母组成的所有单词.

为加快数据处理的速度,借助Hadoop中的MapReduce编程模型的特点,编写出并行计算程序.

1、在 Map
阶段，对每个word（单词）按字母进行排序生成sortedWord，然后输出key/value键值对(sortedWord,word)。
2、在 Reduce 阶段，统计出每组相同字母组成的所有文本

Map代码

package com.itstar.partitiontest;


import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
import java.util.Arrays;


public class wordMap extends Mapper {
     Text sortedText =new Text();
     Text orginalText =new Text();

    @Override
    protected void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        String line = value.toString();
        char[] wordline = line.toCharArray();
        Arrays.sort(wordline);
        String sortword = new String(wordline);
        sortedText.set(sortword);
        orginalText.set(line);
        context.write(sortedText, orginalText);
    }
}

Reduce代码

package com.itstar.partitiontest;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
import java.util.StringTokenizer;

public class wordReduce extends Reducer {
    Text outputKey = new Text();
    Text outputValue = new Text();

    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        String output ="";
        for (Text text:
                values) {
            if (!output.equals("")){
                output =output+",";
            }
            output=output+text.toString();
        }
        StringTokenizer outputTokemizer = new StringTokenizer(output, ",");
        if (outputTokemizer.countTokens() >=2){
            outputKey.set(key.toString());
            outputValue.set(output);
            context.write(outputKey, outputValue);
        }
    }
}

package com.itstar.partitiontest;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 java.io.IOException;

public class wordDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args =new String[]{"F:\Software\Hadoop\input\word.txt","F:\Software\Hadoop\output\outworld"};
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(wordDriver.class);
        job.setMapperClass(wordMap.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        job.setNumReduceTasks(0);
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job,new Path(args[1]));
        job.waitForCompletion(true);
    }
}

该代码没有在Hadoop集群运行,在windows本地运行的.
如需在Hadoop集群运行,将wordDriver里面的代码进行修改.