hadoop map reduce的模板寫法，按照此法，高效節省

寫了很多的hadoop map reduce，發現這程序有必定共用性，能夠作成模板，之後直接拿來改改，就能夠直接開發業務了，通過抽取刪減，大體的模板代碼以下：

 

package com.test.my.common;

import com.test.my.WordCount;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.Tool;
import org.apache.hadoop.util.ToolRunner;

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

public class ModuleMapReduce extends Configured implements Tool{


    public static class ModuleMapper extends Mapper<IntWritable,Text,Text,IntWritable>{
        Text mapKeyText=new Text();
        IntWritable mapValueInt=new IntWritable(1);
        @Override
        protected void map(IntWritable key, Text value, Context context) throws IOException, InterruptedException {
            //TODO
           /* String lineValue=value.toString();

            StringTokenizer stringTokenizer=new StringTokenizer(lineValue);

            while (stringTokenizer.hasMoreTokens()){
                String wordValue=stringTokenizer.nextToken();
                mapKeyText.set(wordValue);
                context.write(mapKeyText,new IntWritable(1));
            }*/
        }
    }

    public static class ModuleReducer extends Reducer<Text,IntWritable,Text,IntWritable>{
        private static IntWritable outValueInt=new IntWritable();
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            //TODO
            int sum=0;
           /* for(IntWritable value:values){
                sum+=value.get();
            }
            outValueInt.set(sum);
            context.write(key,outValueInt);*/
        }
    }

    @Override
    public int run(String[] strings) throws Exception {
        //讀取配置文件
        Configuration configuration=getConf();
        //creat job
        Job job=Job.getInstance(configuration,this.getClass().getSimpleName());
        //run jar
        job.setJarByClass(this.getClass());
        //set job
        //input->map-->reduce-->reduce
        //input set
        Path path=new Path(strings[0]);
        FileInputFormat.addInputPath(job,path);
        //set mapper
        job.setMapperClass(WordCount.WordCountMapper.class);
        //set mapper output
        //TODO
        job.setMapOutputKeyClass(Text.class);
        //TODO
        job.setMapOutputValueClass(IntWritable.class);
        job.setReducerClass(WordCount.WordCountReducer.class);
        //TODO
        job.setOutputKeyClass(Text.class);
        //TODO
        job.setOutputValueClass(IntWritable.class);

        //set output
        Path output=new Path(strings[1]);
        FileOutputFormat.setOutputPath(job,output);

        //submit job
        boolean isSuccess=job.waitForCompletion(true);
        return isSuccess?1:0;
    }

    public static void main(String[] args) throws Exception {
        args=new String[2];
        args[0]="E:\\test\\wcinput.txt";
        args[1]="E:\\test\\output13";
        Configuration configuration=new Configuration();
        int result=ToolRunner.run(configuration,new ModuleMapReduce(),args);
        System.out.println("result:"+result);
    }
}