第二个案例实操——创建Bean类

1.需求&＃xff1a;

准备一个phone_data.txt文档&＃xff0c;内容如下&＃xff1a;

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&＃xff1a;
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 网络状态码输出数据格式&＃xff1a;
13560436666 1116 954 2070
手机号码 上行流量 下行流量 总流量


2.需求分析

1.需求&＃xff1a;统计每一个手机号耗费的总上行流量、下行流量、总流量2.输入数据格式&＃xff1a;
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.输出数据格式&＃xff1a;
13560436666 1116 954 2070
手机号码 上行流量 下行流量 总流量4.Map阶段
(1)读取一行数据&＃xff0c;切分字段
7 13560436666 120.196.100.99 1116 954 200
(2)抽取手机号、上行流量、下行流量
13560436666 1116 954
(3)以手机号key,bean对象为value输出&＃xff0c;即context.write(手机号,bean) 注&＃xff1a;bean对象实现序列化才能传输5.Reduce阶段
累加上行流量和下行流量得到总流量
13560436666 1116 &＃43; 954 &＃61; 2070
手机号码 上行流量 下行流量 总流量


3.编程实现

创建包名&＃xff1a;com.yingzi.mapreduce.writable

3.1.创建Bean类

package com.yingzi.mapreduce.writable;/*** &＃64;author 影子* &＃64;create 2022-01-13-15:57**/import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;/*** 1、定义类实现writable接口* 2、重写序列化和反序列化方法* 3、重写空参构造* 4、toString方法*/
public class FlowBean implements Writable {private long upFlow; //上行流量private long downFlow; //下行流量private long sumFlow; //总流量// 空参构造public FlowBean(){}public long getUpFlow() {return upFlow;}public void setUpFlow(long upFlow) {this.upFlow &＃61; upFlow;}public long getDownFlow() {return downFlow;}public void setDownFlow(long downFlow) {this.downFlow &＃61; downFlow;}public long getSumFlow() {return sumFlow;}public void setSumFlow(long sumFlow) {this.sumFlow &＃61; sumFlow;}public void setSumFlow() {this.sumFlow &＃61; this.upFlow &＃43; this.downFlow;}&＃64;Overridepublic void write(DataOutput dataOutput) throws IOException {dataOutput.writeLong(upFlow);dataOutput.writeLong(downFlow);dataOutput.writeLong(sumFlow);}&＃64;Overridepublic void readFields(DataInput dataInput) throws IOException {this.upFlow &＃61; dataInput.readLong();this.downFlow &＃61; dataInput.readLong();this.sumFlow &＃61; dataInput.readLong();}&＃64;Overridepublic String toString() {return upFlow &＃43; "\t" &＃43; downFlow &＃43; "\t" &＃43; sumFlow;}
}


3.2.创建Mapper类

package com.yingzi.mapreduce.writable;import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;/*** &＃64;author 影子* &＃64;create 2022-01-13-16:12**/
public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> {private Text outK &＃61; new Text();private FlowBean outV &＃61; new FlowBean();&＃64;Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context) throws IOException, InterruptedException {// 1.获取一行// 1 13736230513 192.196.100.1 www.atguigu.com 2481 24681 200String line &＃61; value.toString();// 2.切割// 1,13736230513,192.196.100.1,www.atguigu.com 2481,24681,200String[] split &＃61; line.split("\t");// 3.抓取想要的数据// 手机号&＃xff1a;13736230513// 上行流量&＃xff1a;2481 下行流量&＃xff1a;24681String phone &＃61; split[1];String up &＃61; split[split.length - 3];String down &＃61; split[split.length - 2];// 4.封装outK.set(phone);outV.setUpFlow(Long.parseLong(up));outV.setDownFlow(Long.parseLong(down));outV.setSumFlow();// 5.写出context.write(outK,outV);}
}


3.3.创建Reducer类

package com.yingzi.mapreduce.writable;import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;/*** &＃64;author 影子* &＃64;create 2022-01-13-16:32**/
public class FlowReducer extends Reducer<Text,FlowBean,Text,FlowBean>{private FlowBean outV &＃61; new FlowBean();&＃64;Overrideprotected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context) throws IOException, InterruptedException {// 1.遍历集合累加值long totalUp &＃61; 0;long totalDown &＃61; 0;for (FlowBean value : values) {totalUp &＃43;&＃61; value.getUpFlow();totalDown &＃43;&＃61; value.getDownFlow();}// 2.封装outK,outVoutV.setUpFlow(totalUp);outV.setDownFlow(totalDown);outV.setSumFlow();// 3.写出context.write(key,outV);}
}


3.4.创建Driver类

package com.yingzi.mapreduce.writable;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;
import java.io.IOException;/*** &＃64;author 影子* &＃64;create 2022-01-13-16:40**/
public class FlowDriver {public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {// 1.获取jobConfiguration conf &＃61; new Configuration();Job job &＃61; Job.getInstance(conf);// 2.设置jarjob.setJarByClass(FlowDriver.class);// 3.关联Mapper、Reducerjob.setMapperClass(FlowMapper.class);job.setReducerClass(FlowReducer.class);// 4.设置mapper,输出的key和value类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(FlowBean.class);// 5.设置最终数据输出的key和value类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(FlowBean.class);// 6.设置数据的输入和输出路径FileInputFormat.setInputPaths(job,new Path("G:\\计算机资料\\大数据开发\\尚硅谷大数据技术之Hadoop3.x\\资料\\11_input\\inputflow"));FileOutputFormat.setOutputPath(job,new Path("G:\\计算机资料\\大数据开发\\尚硅谷大数据技术之Hadoop3.x\\资料\\_output\\output1"));// 7.提交jobboolean result &＃61; job.waitForCompletion(true);System.exit(result ? 0:1);}
}


4.查看结果

说明&＃xff1a;上传至集群同第一个案例最后操作一样&＃xff0c;就不再操作了
链接如下&＃xff1a;https://blog.csdn.net/mynameisgt/article/details/122484444