大数据之Hadoop(MapReduce)： 序列化案例实操

Posted 2021-06-20 浊酒南街

目录

• 1、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
• 2.需求分析
• 3.编写MapReduce程序

1、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

（1）输入数据:phone_data.txt

1	13736230513	192.196.100.1	www.jinghang.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.jinghang.com	1527	2106	200
6 	18271575951	192.168.100.3	www.jinghang.com	4116	1432	200
7 	18271575951	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 	13630577991	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.jinghang.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

（2）输入数据格式：

7	13560436666	120.196.100.99	1116	954	200
id	手机号码	网络ip	上行流量	下行流量	网络状态码

（3）期望输出数据格式

13560436666	1116	954	2070
手机号码	上行流量	下行流量	总流量

2.需求分析

3.编写MapReduce程序

（1）编写流量统计的Bean对象

package com.jinghang.mapreduce.flowsum;
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;
	}
}

（2）编写Mapper类

package com.jinghang.mapreduce.flowsum;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{	
	FlowBean v = new FlowBean();
	Text k = new Text();
	@Override
	protected void map(LongWritable key, Text value, Context context)	throws IOException, InterruptedException {	
		// 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.set(downFlow, upFlow);
		// 4 写出
		context.write(k, v);
	}
}

（3）编写Reducer类

package com.jinghang.mapreduce.flowsum;
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, Context context)throws IOException, InterruptedException {

		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);
	}
}

（4）编写Driver驱动类

package com.jinghang.mapreduce.flowsum;
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:/input/inputflow", "e:/output1" };
		// 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);
	}
}