Hadoop之——使用hadoop自定义类型处置手机上网日志

Hadoop之——使用hadoop自定义类型处理手机上网日志

转载请注明出处:http://blog.****.net/l1028386804/article/details/46046517

不多说，直接上代码

package com.lyz.hadoop.count;

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

import org.apache.hadoop.conf.Configuration;
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;

/**
 * 利用Hadoop MapRedduce
 * @author liuyazhuang
 *
 */
public class KpiApp {
	/**
	 * 输入地址的路径
	 */
	private static final String INPUT_PATH = "hdfs://liuyazhuang:9000/d1/wlan";
	/**
	 * 计算结果输出的路径
	 */
	private static final String OUT_PATH = "hdfs://liuyazhuang:9000/d1/out";
	
	public static void main(String[] args) throws Exception{
		//实例化Job对象
		Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());
		//1.1指定输入文件路径
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		//指定格式化输入文件的类
		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(HashPartitioner.class);
		//指定任务数量
		job.setNumReduceTasks(1);
		
		//1.4 TODO 排序，分区
		
		//1.5 TODO 合并(可选)
		
		//2.2指定自定义的reducer类
		job.setReducerClass(MyReducer.class);
		//指定输出的<k3, v3>类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(KpiWritable.class);
		
		//2.3指定输出的位置
		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		//指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);
	}
	
	/**
	 * Mapper
	 * @author liuyazhuang
	 *
	 */
	static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{
		@Override
		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, KpiWritable>.Context context)throws IOException, InterruptedException {
			String[] splited = value.toString().split("\t");
			String msis = splited[1];
			Text k2 = new Text(msis);
			KpiWritable v2 = new KpiWritable(Long.parseLong(splited[6]), Long.parseLong(splited[7]), Long.parseLong(splited[8]), Long.parseLong(splited[9]));
			context.write(k2, v2);
		}
	}
	
	/**
	 * Reducer
	 * @author liuyazhuang
	 *
	 */
	static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{
		@Override
		protected void reduce(Text k2, Iterable<KpiWritable> v2s, Reducer<Text, KpiWritable, Text, KpiWritable>.Context context) 
				throws IOException, InterruptedException {
			long upPackNum = 0;
			long downPackNum = 0;
			long upPayLoad = 0;
			long downPayLoad = 0;
			for (KpiWritable kpiWritable : v2s) {
				upPackNum += kpiWritable.upPackNum;
				downPackNum += kpiWritable.downPackNum;
				upPayLoad += kpiWritable.upPayLoad;
				downPayLoad += kpiWritable.downPayLoad;
			}
			KpiWritable v3 = new KpiWritable(upPackNum, downPackNum, upPayLoad, downPayLoad);
			context.write(k2, v3);
		}
	}
}

/**
 * 自定义Hadoop数据类型
 * @author liuyazhuang
 *
 */
class KpiWritable implements Writable{
	
	long upPackNum;
	long downPackNum;
	long upPayLoad;
	long downPayLoad;
	
	public KpiWritable() {
		super();
	}
	
	public KpiWritable(long upPackNum, long downPackNum, long upPayLoad, long downPayLoad) {
		super();
		this.upPackNum = upPackNum;
		this.downPackNum = downPackNum;
		this.upPayLoad = upPayLoad;
		this.downPayLoad = downPayLoad;
	}

	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upPackNum);
		out.writeLong(downPackNum);
		out.writeLong(upPayLoad);
		out.writeLong(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 String toString() {
		return "KpiWritable [upPackNum=" + upPackNum + ", downPackNum="
				+ downPackNum + ", upPayLoad=" + upPayLoad + ", downPayLoad="
				+ downPayLoad + "]";
	}
}

注意：
    (1)在eclipse中调用的job.waitForCompletion(true)实际上执行如下方法
        connect();
        info = jobClient.submitJobInternal(conf);
    (2)在connect()方法中，实际上创建了一个JobClient对象。
        在调用该对象的构造方法时，获得了JobTracker的客户端代理对象JobSubmissionProtocol。
        JobSubmissionProtocol的实现类是JobTracker。
    (3)在jobClient.submitJobInternal(conf)方法中，调用了
        JobSubmissionProtocol.submitJob(...),
        即执行的是JobTracker.submitJob(...)。
    (4)Hadoop的数据类型要求必须实现Writable接口。
    (5)java基本类型与Hadoop常见基本类型的对照
        Long    LongWritable
        Integer    IntWritable
        Boolean    BooleanWritable
        String    Text
    java类型如何转化为hadoop基本类型
         调用hadoop类型的构造方法，或者调用set()方法。
        new LongWritable(123L);
    hadoop基本类型如何转化为java类型
         对于Text，需要调用toString()方法，其他类型调用get()方法。