mapreduce实现分组求最大

package cn.itcastcat.bigdata.secondarysort;

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;

/**
 * 利用reduce端的GroupingComparator来实现将一组bean看成相同的key
 * 
 *
 */
public class ItemidGroupingComparator extends WritableComparator {

    //传入作为key的bean的class类型，以及制定需要让框架做反射获取实例对象
    protected ItemidGroupingComparator() {
        super(OrderBean.class, true);
    }
    

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        OrderBean abean = (OrderBean) a;
        OrderBean bbean = (OrderBean) b;
        
        //比较两个bean时，指定只比较bean中的orderid
        return abean.getItemid().compareTo(bbean.getItemid());
        
    }

}

package cn.itcastcat.bigdata.secondarysort;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.Partitioner;


public class ItemIdPartitioner extends Partitioner<OrderBean, NullWritable>{

    @Override
    public int getPartition(OrderBean bean, NullWritable value, int numReduceTasks) {
        //相同id的订单bean，会发往相同的partition
        //而且，产生的分区数，是会跟用户设置的reduce task数保持一致
        return (bean.getItemid().hashCode() & Integer.MAX_VALUE) % numReduceTasks;
        
    }

}

package cn.itcastcat.bigdata.secondarysort;

import java.io.IOException;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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 com.sun.xml.bind.v2.schemagen.xmlschema.List;

/**
 * 
 * @author 
 *
 */
public class SecondarySort {
    
    static class SecondarySortMapper extends Mapper<LongWritable, Text, OrderBean, NullWritable>{
        
        OrderBean bean = new OrderBean();
        
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString();
            String[] fields = StringUtils.split(line, ",");
            
            bean.set(new Text(fields[0]), new DoubleWritable(Double.parseDouble(fields[2])));
            
            context.write(bean, NullWritable.get());
            
        }
        
    }
    
    static class SecondarySortReducer extends Reducer<OrderBean, NullWritable, OrderBean, NullWritable>{
        
        
        //到达reduce时，相同id的所有bean已经被看成一组，且金额最大的那个一排在第一位
        @Override
        protected void reduce(OrderBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }
    
    
    public static void main(String[] args) throws Exception {
        
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        
        job.setJarByClass(SecondarySort.class);
        
        job.setMapperClass(SecondarySortMapper.class);
        job.setReducerClass(SecondarySortReducer.class);
        
        
        job.setOutputKeyClass(OrderBean.class);
        job.setOutputValueClass(NullWritable.class);
        
        FileInputFormat.setInputPaths(job, new Path("D:\srcdata\d10\orders.txt"));
        FileOutputFormat.setOutputPath(job, new Path("D:/srcdata/d10/gpoutput"));
        
        //在此设置自定义的Groupingcomparator类 
        job.setGroupingComparatorClass(ItemidGroupingComparator.class);
        //在此设置自定义的partitioner类
        job.setPartitionerClass(ItemIdPartitioner.class);
        
        job.setNumReduceTasks(2);
        
        job.waitForCompletion(true);
        
    }

}