1、先导知识

TreeMap底层是根据红黑树的数据结构构建的，默认是根据key的自然排序来组织（比如integer的大小，String的字典排序），如果key是自定义类，可以通过重写compareTo方法自定义排序。

firstKey ()方法用于返回此TreeMap中具有最小键值的第一个键元素。.

lastKey ()方法用于返回此TreeMap中具有最大键值的最后一个键元素。.

2、案例

2.1 需求

2.2 代码实现

setup()与cleanup()方法：

　1、setup()，此方法被MapReduce框架仅且执行一次，在执行Map任务前，进行相关变量或者资源的集中初始化工作。若是将资源初始化工作放在方法map()中，导致Mapper任务在解析每一行输入时都会进行资源初始化工作，导致重复，程序运行效率不高！

　2、cleanup(),此方法被MapReduce框架仅且执行一次，在执行完毕Map任务后，进行相关变量或资源的释放工作。若是将释放资源工作放入方法map()中，也会导致Mapper任务在解析、处理每一行文本后释放资源，而且在下一行文本解析前还要重复初始化，导致反复重复，程序运行效率不高！

这里就使用了cleanup方法，map方法和reduce方法保持TreeMap只有n个元素；cleanup用于输出TreeMap的元素给下一个环节用，只需要执行一次，就放在cleanup。

FlowBean类

package com.atguigu.mr.top;

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

import org.apache.hadoop.io.WritableComparable;

public class FlowBean implements WritableComparable<FlowBean>{

	private long upFlow;
	private long downFlow;
	private long sumFlow;
	
	
	public FlowBean() {
		super();
	}

	public FlowBean(long upFlow, long downFlow) {
		super();
		this.upFlow = upFlow;
		this.downFlow = downFlow;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upFlow);
		out.writeLong(downFlow);
		out.writeLong(sumFlow);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		upFlow = in.readLong();
		downFlow = in.readLong();
		sumFlow = in.readLong();
	}

	public long getUpFlow() {
		return upFlow;
	}

	public void setUpFlow(long upFlow) {
		this.upFlow = upFlow;
	}

	public long getDownFlow() {
		return downFlow;
	}

	public void setDownFlow(long downFlow) {
		this.downFlow = downFlow;
	}

	public long getSumFlow() {
		return sumFlow;
	}

	public void setSumFlow(long sumFlow) {
		this.sumFlow = sumFlow;
	}

	@Override
	public String toString() {
		return upFlow + "\t" + downFlow + "\t" + sumFlow;
	}

	public void set(long downFlow2, long upFlow2) {
		downFlow = downFlow2;
		upFlow = upFlow2;
		sumFlow = downFlow2 + upFlow2;
	}

	@Override
	public int compareTo(FlowBean bean) {
		
		int result;
		
		if (this.sumFlow > bean.getSumFlow()) {
			result = -1;
		}else if (this.sumFlow < bean.getSumFlow()) {
			result = 1;
		}else {
			result = 0;
		}
		
		return result;
	}
}

Mapper类

package com.atguigu.mr.top;

import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TopNMapper extends Mapper<LongWritable, Text, FlowBean, Text>{
	
	// 定义一个TreeMap作为存储数据的容器（天然按key排序）
	private TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();
	private FlowBean kBean;
	
	@Override
	protected void map(LongWritable key, Text value, Context context)	throws IOException, InterruptedException {
		
		kBean = new FlowBean();
		Text v = new Text();
		
		// 1 获取一行
		String line = value.toString();
		
		// 2 切割
		String[] fields = line.split("\t");
		
		// 3 封装数据
		String phoneNum = fields[0];
		long upFlow = Long.parseLong(fields[1]);
		long downFlow = Long.parseLong(fields[2]);
		long sumFlow = Long.parseLong(fields[3]);
		
		kBean.setDownFlow(downFlow);
		kBean.setUpFlow(upFlow);
		kBean.setSumFlow(sumFlow);
		
		v.set(phoneNum);
		
		// 4 向TreeMap中添加数据
		flowMap.put(kBean, v);
		
		// 5 限制TreeMap的数据量，超过10条就删除掉流量最小的一条数据
		if (flowMap.size() > 10) {
//		flowMap.remove(flowMap.firstKey());
			flowMap.remove(flowMap.lastKey());		
}
	}
	
	@Override
	protected void cleanup(Context context) throws IOException, InterruptedException {
		
		// 6 遍历treeMap集合，输出数据
		Iterator<FlowBean> bean = flowMap.keySet().iterator();

		while (bean.hasNext()) {

			FlowBean k = bean.next();

			context.write(k, flowMap.get(k));
		}
	}
}

Reducer类

package com.atguigu.mr.top;

import java.io.IOException;
import java.util.Iterator;
import java.util.TreeMap;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TopNReducer extends Reducer<FlowBean, Text, Text, FlowBean> {

	// 定义一个TreeMap作为存储数据的容器（天然按key排序）
	TreeMap<FlowBean, Text> flowMap = new TreeMap<FlowBean, Text>();

	@Override
	protected void reduce(FlowBean key, Iterable<Text> values, Context context)throws IOException, InterruptedException {

		for (Text value : values) {

			 FlowBean bean = new FlowBean();
			 bean.set(key.getDownFlow(), key.getUpFlow());

			 // 1 向treeMap集合中添加数据
			flowMap.put(bean, new Text(value));

			// 2 限制TreeMap数据量，超过10条就删除掉流量最小的一条数据
			if (flowMap.size() > 10) {
				// flowMap.remove(flowMap.firstKey());
flowMap.remove(flowMap.lastKey());
			}
		}
	}

	@Override
	protected void cleanup(Reducer<FlowBean, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {

		// 3 遍历集合，输出数据
		Iterator<FlowBean> it = flowMap.keySet().iterator();

		while (it.hasNext()) {

			FlowBean v = it.next();

			context.write(new Text(flowMap.get(v)), v);
		}
	}
}

Driver类

package com.atguigu.mr.top;

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;

public class TopNDriver {

	public static void main(String[] args) throws Exception {
		
		args  = new String[]{"e:/output1","e:/output3"};
		
		// 1 获取配置信息，或者job对象实例
		Configuration configuration = new Configuration();
		Job job = Job.getInstance(configuration);

		// 6 指定本程序的jar包所在的本地路径
		job.setJarByClass(TopNDriver.class);

		// 2 指定本业务job要使用的mapper/Reducer业务类
		job.setMapperClass(TopNMapper.class);
		job.setReducerClass(TopNReducer.class);

		// 3 指定mapper输出数据的kv类型
		job.setMapOutputKeyClass(FlowBean.class);
		job.setMapOutputValueClass(Text.class);

		// 4 指定最终输出的数据的kv类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(FlowBean.class);

		// 5 指定job的输入原始文件所在目录
		FileInputFormat.setInputPaths(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));

		// 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行
		boolean result = job.waitForCompletion(true);
		System.exit(result ? 0 : 1);
	}
}