Hadoop序列化案例实操

需求

统计每一个手机号耗费的总上行流量、下行流量、总流量。

输入数据：

1    13736230513    192.196.100.1    www.atguigu.com    2481    24681    200
2    13846544121    192.196.100.2            264    0    200
3     13956435636    192.196.100.3            132    1512    200
4     13966251146    192.168.100.1            240    0    404
5     18271575951    192.168.100.2    www.atguigu.com    1527    2106    200
6     84188413    192.168.100.3    www.atguigu.com    4116    1432    200
7     13590439668    192.168.100.4            1116    954    200
8     15910133277    192.168.100.5    www.hao123.com    3156    2936    200
9     13729199489    192.168.100.6            240    0    200
10     13630577991    192.168.100.7    www.shouhu.com    6960    690    200
11     15043685818    192.168.100.8    www.baidu.com    3659    3538    200
12     15959002129    192.168.100.9    www.atguigu.com    1938    180    500
13     13560439638    192.168.100.10            918    4938    200
14     13470253144    192.168.100.11            180    180    200
15     13682846555    192.168.100.12    www.qq.com    1938    2910    200
16     13992314666    192.168.100.13    www.gaga.com    3008    3720    200
17     13509468723    192.168.100.14    www.qinghua.com    7335    110349    404
18     18390173782    192.168.100.15    www.sogou.com    9531    2412    200
19     13975057813    192.168.100.16    www.baidu.com    11058    48243    200
20     13768778790    192.168.100.17            120    120    200
21     13568436656    192.168.100.18    www.alibaba.com    2481    24681    200
22     13568436656    192.168.100.19            1116    954    200

输入数据格式：

期望输出数据格式：

需求分析

编写MapReduce程序

流量统计的Bean对象：

package com.mcq;

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

import org.apache.hadoop.io.Writable;

// 1 实现writable接口
public class FlowBean implements Writable {

	private long upFlow;
	private long downFlow;
	private long sumFlow;

	// 2 反序列化时，需要反射调用空参构造函数，所以必须有
	public FlowBean() {
		super();
	}

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

	// 3 写序列化方法
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upFlow);
		out.writeLong(downFlow);
		out.writeLong(sumFlow);
	}

	// 4 反序列化方法
	// 5 反序列化方法读顺序必须和写序列化方法的写顺序必须一致
	@Override
	public void readFields(DataInput in) throws IOException {
		this.upFlow = in.readLong();
		this.downFlow = in.readLong();
		this.sumFlow = in.readLong();
	}

	// 6 编写toString方法，方便后续打印到文本
	@Override
	public String toString() {
		return upFlow + "\t" + downFlow + "\t" + sumFlow;
	}

	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;
	}
}

Mapper类：

package com.mcq;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import com.sun.jersey.spi.StringReader.ValidateDefaultValue;

public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
	Text k = new Text();
	FlowBean v = new FlowBean();

	@Override
	protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context)
			throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		// 1 获取一行
		String line = value.toString();

		// 2 切割字段
		String[] fields = line.split("\t");

		// 3 封装对象
		// 取出手机号码
		String phoneNum = fields[1];

		// 取出上行流量和下行流量
		long upFlow = Long.parseLong(fields[fields.length - 3]);
		long downFlow = Long.parseLong(fields[fields.length - 2]);

		k.set(phoneNum);
		v.setUpFlow(upFlow);
		v.setDownFlow(downFlow);

		// 4 写出
		context.write(k, v);
	}
}

Reducer类：

package com.mcq;

import java.io.IOException;

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

public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
	@Override
	protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
			throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		long sum_upFlow = 0;
		long sum_downFlow = 0;

		// 1 遍历所用bean，将其中的上行流量，下行流量分别累加
		for (FlowBean flowBean : values) {
			sum_upFlow += flowBean.getUpFlow();
			sum_downFlow += flowBean.getDownFlow();
		}

		// 2 封装对象
		FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow);

		// 3 写出
		context.write(key, resultBean);

	}
}

驱动类：

package com.mcq;

import java.io.IOException;

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 FlowsumDriver {
	public static void main(String[] args) throws IllegalArgumentException, IOException, ClassNotFoundException, InterruptedException {
		// 输入输出路径需要根据自己电脑上实际的输入输出路径设置
		args = new String[] { "e:/mrtest/phone_data.txt", "e:/mrtest/output" };

		// 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(FlowsumDriver.class);

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

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