Hadoop之——Partitioner编程

转载请注明出处:http://blog.csdn.net/l1028386804/article/details/46136685

一、Mapper类的实现

	static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{
		protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
			final String[] splited = value.toString().split("\t");
			final String msisdn = splited[1];
			final Text k2 = new Text(msisdn);
			final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);
			context.write(k2, v2);
		};
	}

二、Reducer类的实现

	static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{
		/**
		 * @param	k2	表示整个文件中不同的手机号码
		 * @param	v2s	表示该手机号在不同时段的流量的集合
		 */
		protected void reduce(Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text,KpiWritable,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
			long upPackNum = 0L;
			long downPackNum = 0L;
			long upPayLoad = 0L;
			long downPayLoad = 0L;

			for (KpiWritable kpiWritable : v2s) {
				upPackNum += kpiWritable.upPackNum;
				downPackNum += kpiWritable.downPackNum;
				upPayLoad += kpiWritable.upPayLoad;
				downPayLoad += kpiWritable.downPayLoad;
			}

			final KpiWritable v3 = new KpiWritable(upPackNum+"", downPackNum+"", upPayLoad+"", downPayLoad+"");
			context.write(k2, v3);
		};
	}

三、Partitioner类的实现

	static class KpiPartitioner extends HashPartitioner<Text, KpiWritable>{
		@Override
		public int getPartition(Text key, KpiWritable value, int numReduceTasks) {
			return (key.toString().length()==11)?0:1;
		}
	}
}

四、自定义Hadoop数据类型

class KpiWritable implements Writable{
	long upPackNum;
	long downPackNum;
	long upPayLoad;
	long downPayLoad;

	public KpiWritable(){}

	public KpiWritable(String upPackNum, String downPackNum, String upPayLoad, String downPayLoad){
		this.upPackNum = Long.parseLong(upPackNum);
		this.downPackNum = Long.parseLong(downPackNum);
		this.upPayLoad = Long.parseLong(upPayLoad);
		this.downPayLoad = Long.parseLong(downPayLoad);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.upPackNum = in.readLong();
		this.downPackNum = in.readLong();
		this.upPayLoad = in.readLong();
		this.downPayLoad = in.readLong();
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upPackNum);
		out.writeLong(downPackNum);
		out.writeLong(upPayLoad);
		out.writeLong(downPayLoad);
	}

	@Override
	public String toString() {
		return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad;
	}

五、程序入口Main方法

	public static void main(String[] args) throws Exception{
		Configuration conf = new Configuration();
		final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
		final Path outPath = new Path(OUT_PATH);
		//如果已经存在输出文件，则先删除已存在的输出文件
		if(fileSystem.exists(outPath)){
			fileSystem.delete(outPath, true);
		}
		final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
		//打成jar包
		job.setJarByClass(KpiApp.class);

		//1.1 指定输入文件路径
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		//指定哪个类用来格式化输入文件
		job.setInputFormatClass(TextInputFormat.class);

		//1.2指定自定义的Mapper类
		job.setMapperClass(MyMapper.class);
		//指定输出<k2,v2>的类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(KpiWritable.class);

		//1.3 指定分区类
		job.setPartitionerClass(KpiPartitioner.class);
		job.setNumReduceTasks(2);

		//1.4 TODO 排序、分区

		//1.5  TODO （可选）合并

		//2.2 指定自定义的reduce类
		job.setReducerClass(MyReducer.class);
		//指定输出<k3,v3>的类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(KpiWritable.class);

		//2.3 指定输出到哪里
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		//设定输出文件的格式化类
		job.setOutputFormatClass(TextOutputFormat.class);

		//把代码提交给JobTracker执行
		job.waitForCompletion(true);
	}

六、完整程序

package com.lyz.hadoop.p;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
/**
 * 分区的例子必须打成jar运行
 * 用处 1.根据业务需要，产生多个输出文件
 * 		2.多个reduce任务在运行，提高整体job的运行效率
 */
public class KpiApp {
	static final String INPUT_PATH = "hdfs://liuyazhuang:9000/wlan";
	static final String OUT_PATH = "hdfs://liuyazhuang:9000/out";
	public static void main(String[] args) throws Exception{
		Configuration conf = new Configuration();
		final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
		final Path outPath = new Path(OUT_PATH);
		//如果已经存在输出文件，则先删除已存在的输出文件
		if(fileSystem.exists(outPath)){
			fileSystem.delete(outPath, true);
		}
		final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
		//打成jar包
		job.setJarByClass(KpiApp.class);

		//1.1 指定输入文件路径
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		//指定哪个类用来格式化输入文件
		job.setInputFormatClass(TextInputFormat.class);

		//1.2指定自定义的Mapper类
		job.setMapperClass(MyMapper.class);
		//指定输出<k2,v2>的类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(KpiWritable.class);

		//1.3 指定分区类
		job.setPartitionerClass(KpiPartitioner.class);
		job.setNumReduceTasks(2);

		//1.4 TODO 排序、分区

		//1.5  TODO （可选）合并

		//2.2 指定自定义的reduce类
		job.setReducerClass(MyReducer.class);
		//指定输出<k3,v3>的类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(KpiWritable.class);

		//2.3 指定输出到哪里
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		//设定输出文件的格式化类
		job.setOutputFormatClass(TextOutputFormat.class);

		//把代码提交给JobTracker执行
		job.waitForCompletion(true);
	}

	static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{
		protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
			final String[] splited = value.toString().split("\t");
			final String msisdn = splited[1];
			final Text k2 = new Text(msisdn);
			final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);
			context.write(k2, v2);
		};
	}

	static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{
		/**
		 * @param	k2	表示整个文件中不同的手机号码
		 * @param	v2s	表示该手机号在不同时段的流量的集合
		 */
		protected void reduce(Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text,KpiWritable,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {
			long upPackNum = 0L;
			long downPackNum = 0L;
			long upPayLoad = 0L;
			long downPayLoad = 0L;

			for (KpiWritable kpiWritable : v2s) {
				upPackNum += kpiWritable.upPackNum;
				downPackNum += kpiWritable.downPackNum;
				upPayLoad += kpiWritable.upPayLoad;
				downPayLoad += kpiWritable.downPayLoad;
			}

			final KpiWritable v3 = new KpiWritable(upPackNum+"", downPackNum+"", upPayLoad+"", downPayLoad+"");
			context.write(k2, v3);
		};
	}

	static class KpiPartitioner extends HashPartitioner<Text, KpiWritable>{
		@Override
		public int getPartition(Text key, KpiWritable value, int numReduceTasks) {
			return (key.toString().length()==11)?0:1;
		}
	}
}

class KpiWritable implements Writable{
	long upPackNum;
	long downPackNum;
	long upPayLoad;
	long downPayLoad;

	public KpiWritable(){}

	public KpiWritable(String upPackNum, String downPackNum, String upPayLoad, String downPayLoad){
		this.upPackNum = Long.parseLong(upPackNum);
		this.downPackNum = Long.parseLong(downPackNum);
		this.upPayLoad = Long.parseLong(upPayLoad);
		this.downPayLoad = Long.parseLong(downPayLoad);
	}

	@Override
	public void readFields(DataInput in) throws IOException {
		this.upPackNum = in.readLong();
		this.downPackNum = in.readLong();
		this.upPayLoad = in.readLong();
		this.downPayLoad = in.readLong();
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upPackNum);
		out.writeLong(downPackNum);
		out.writeLong(upPayLoad);
		out.writeLong(downPayLoad);
	}

	@Override
	public String toString() {
		return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad;
	}
}