九(三):手机上网流量汇总求和(排序方式二)

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

public class FlowBean implements WritableComparable<FlowBean>{

	//电话号码
	private String phoneNb;
	//上传流量
	private long flow_up;
	//下载流量
	private long flow_down;
	//总流量
	private long flow_sum;
	
	public String getPhoneNb() {
		return phoneNb;
	}
	public void setPhoneNb(String phoneNb) {
		this.phoneNb = phoneNb;
	}
	public long getFlow_up() {
		return flow_up;
	}
	public void setFlow_up(long flow_up) {
		this.flow_up = flow_up;
	}
	public long getFlow_down() {
		return flow_down;
	}
	public void setFlow_down(long flow_down) {
		this.flow_down = flow_down;
	}
	public long getFlow_sum() {
		return flow_sum;
	}
	public void setFlow_sum(long flow_sum) {
		this.flow_sum = flow_sum;
	}
	//注意：在hadoop使用当中，如果你要使用有参的构造方法，无参的构造方法必须写出来。
	public FlowBean() {
		
	}
	//有参的构造方法
	public FlowBean(String phoneNb, long flow_up, long flow_down) {
		this.phoneNb = phoneNb;
		this.flow_up = flow_up;
		this.flow_down = flow_down;
		this.flow_sum = flow_up+flow_down;
	}
	
	/**
	 * 序列化对象：把结构化对象转化为字节流
	 */
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(phoneNb);
		out.writeLong(flow_up);
		out.writeLong(flow_down);
		out.writeLong(flow_sum);
	}
	/**
	 * 反序列化对象：把字节流化为结构化对象
	 */
	@Override
	public void readFields(DataInput in) throws IOException {
		this.phoneNb = in.readUTF();
		this.flow_up = in.readLong();
		this.flow_down = in.readLong();
		this.flow_sum = in.readLong();
	}
	@Override
	public String toString() {
		return "" + flow_up + "\t" + flow_down+ "\t" + flow_sum + "";
	}
	@Override
	public int compareTo(FlowBean bean) {
		return this.flow_sum > bean.getFlow_sum() ? -1 : 1;
	}
	
}

import java.io.IOException;
import java.util.UUID;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.Partitioner;
import org.apache.hadoop.mapred.lib.HashPartitioner;
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 cn.com.hadoop.mr.flowsum.FlowBean;

public class FlowSumSortRunner {
	
	public static void main(String[] args) throws Exception {
		Job job = Job.getInstance(new Configuration());
		
		job.setJarByClass(FlowSumSortRunner.class);
		job.setMapperClass(FlowSumSortMapper.class);
		job.setReducerClass(FlowSumSortReducer.class);
		
		job.setMapOutputKeyClass(FlowBean.class);
		job.setMapOutputValueClass(NullWritable.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(FlowBean.class);
		//输入第一次未排序的结果,然后在执行mapreduce程序就进行排序了
		FileInputFormat.setInputPaths(job, new Path("hdfs://XXX:9000/flowsum/part-r-00000"));
		FileOutputFormat.setOutputPath(job, new Path("hdfs://xxx:9000/"+UUID.randomUUID()));
		
		job.waitForCompletion(true);
	}
	
	public static class FlowSumSortMapper extends Mapper<LongWritable, Text, FlowBean, NullWritable>{

		@Override
		protected void map(LongWritable key,Text value,
				Mapper<LongWritable, Text, FlowBean, NullWritable>.Context context)
				throws IOException, InterruptedException {
			String line = value.toString();
			String[] split = StringUtils.split(line, "\t");
			context.write(new FlowBean(split[0], Long.parseLong(split[1]), Long.parseLong(split[2])), NullWritable.get());
		}
		
		
		
	}
	
	public static class FlowSumSortReducer extends Reducer<FlowBean, NullWritable, Text, FlowBean>{

		@Override
		protected void reduce(FlowBean key, Iterable<NullWritable> values,
				Reducer<FlowBean, NullWritable, Text, FlowBean>.Context context)
				throws IOException, InterruptedException {
			context.write(new Text(key.getPhoneNb()), key);
			
		}

		
	}
}