Hadoop序列化案例实操

Hadoop 序列化案例实操

1 需求与分析

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

1 13736230513 192.196.100.1 www.dev1.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.dev1.com  1527  2106  200
6   84188413  192.168.100.3 www.dev1.com  4116  1432  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

（4）Map阶段

4.1 读一行数据，切分字段

7     13560436666    120.196.100.99        1116         954            200

4.2 抽取手机号，上行流量，下行流量

13560436666    1116 954

4.3 以手机号为key,bean对象为value输出，即​​context.write(手机号,bean对象)​​​ 4.4 bean对象能传输必须实现序列化接口
（5）Reduce阶段
5.1 累加上行流量和下行流量的总和

13560436666    1116  +   954  =2070
手机号，   上行流量，下行流量 

序列化案例实操

（1）编写流量统计的Bean对象

package com.dev1.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;
    }

    //此处省略getter/setter
}

（2）编写Mapper类

package com.dev1.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.dev1.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.dev1.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);
    }
}

提交到集群

hdfs dfs -mkdir -p /user/dev1/input1
hdfs dfs -put phone_data.txt /user/dev1/input1
hadoop jar ./flowsum.jar com.dev1.flowsum.FlowsumDriver /user/dev1/input1  /user/dev1/output1
hdfs dfs -cat /user/dev1/output1/*