Fork me on GitHub

MapReduce基础学习-二

发表于

Hadoop序列化

序列化概述

image
image

自定义bean对象实现序列化接口（Writable）

在企业开发中往往常用的基本序列化类型不能满足所有需求，比如在Hadoop框架内部传递一个bean对象，那么该对象就需要实现序列化接口。
具体实现bean对象序列化步骤如下7步。

  1. 必须实现Writable接口
  2. 反序列化时，需要反射调用空参构造函数，所以必须有空参构造
1
2
3
public FlowBean() {
	super();
}
  1. 重写序列化方法
1
2
3
4
5
6
@Override
public void write(DataOutput out) throws IOException {
	out.writeLong(upFlow);
	out.writeLong(downFlow);
	out.writeLong(sumFlow);
}
  1. 重写反序列化方法
1
2
3
4
5
6
@Override
public void readFields(DataInput in) throws IOException {
	upFlow = in.readLong();
	downFlow = in.readLong();
	sumFlow = in.readLong();
}
  1. 注意反序列化的顺序和序列化的顺序完全一致
  2. 要想把结果显示在文件中，需要重写toString()，可用”\t”分开，方便后续用。
  3. 如果需要将自定义的bean放在key中传输，则还需要实现Comparable接口，因为MapReduce框中的Shuffle过程要求对key必须能排序。
1
2
3
4
5
@Override
public int compareTo(FlowBean o) {
	// 倒序排列，从大到小
	return this.sumFlow > o.getSumFlow() ? -1 : 1;
}

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

  1. 输入数据(phone_data.txt)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
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
  1. 输入数据格式
1
2
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
  1. 期望输出数据格式
1
2
13560436666 		1116		      954 			2070
手机号码		        上行流量        下行流量		总流量
需求分析

image

编写MapReduce程序
  1. 编写流量统计的Bean对象
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
package com.atyp.mr.flowsum;

import org.apache.hadoop.io.Writable;

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

public class FlowBean implements Writable {
    //上行流量
    private long upFlow;
    //下行流量
    private long downFlow;
    //总流量
    private long sumFlow;

    /**
     * 空参构造，为了后续反射用
     */
    public FlowBean() {
    }

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

    /**
     * 序列化方法
     *
     * @param dataOutput
     * @throws IOException
     */
    @Override
    public void write(DataOutput dataOutput) throws IOException {
        dataOutput.writeLong(upFlow);
        dataOutput.writeLong(downFlow);
        dataOutput.writeLong(sumFlow);
    }

    /**
     * 反序列化方法
     *
     * @param dataInput
     * @throws IOException
     */
    @Override
    public void readFields(DataInput dataInput) throws IOException {
        //必须要求和序列化方法顺序一致
        upFlow = dataInput.readLong();
        downFlow = dataInput.readLong();
        sumFlow = dataInput.readLong();
    }

    @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;
    }
}
  1. 编写Mapper类
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
package com.atyp.mr.flowsum;

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

import java.io.IOException;

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, Context context) throws IOException, InterruptedException {
        //22 	13568436656	192.168.100.19	 1116	 954	    200

        // 1.获取一行
        String line = value.toString();

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

        // 3.封装对象
        k.set(fields[1]); //封装手机

        long upFlow = Long.parseLong(fields[fields.length - 3]);
        long downFlow = Long.parseLong(fields[fields.length - 2]);
        v.setUpFlow(upFlow);
        v.setDownFlow(downFlow);
        //v.set(upFlow, downFlow);

        // 4.写出
        context.write(k, v);
    }
}
  1. 编写Reducer类
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
package com.atyp.mr.flowsum;

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

import java.io.IOException;

public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
    FlowBean v = new FlowBean();
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
        //13568436656	192.168.100.18	    2481	24681
        //13568436656	192.168.100.19		1116	954

        long sum_upFlow = 0;
        long sum_downFlow = 0;
        // 1.累加求和
        for (FlowBean value : values) {
            sum_upFlow += value.getUpFlow();
            sum_downFlow += value.getDownFlow();
        }

        v.set(sum_upFlow, sum_downFlow);

        // 2.写出
        context.write(key, v);
    }
}
  1. 编写Driver驱动类
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
package com.atyp.mr.flowsum;

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;

public class FlowSumDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"D:\\WorkSpace\\IdeaWorpace\\hadoop_input_output\\input_02","D:\\WorkSpace\\IdeaWorpace\\hadoop_input_output\\output_02"};
        Configuration conf = new Configuration();
        // 1.获取Job对象
        Job job = Job.getInstance(conf);

        // 2.设置jar的路径
        job.setJarByClass(FlowSumDriver.class);

        // 3.关联Mapper和Reducer
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

        // 4.设置mapper输出的key和value类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        // 5.设置最终输出的key和value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 6.设置输入输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        // 7.提交Job
        boolean result = job.waitForCompletion(true);

        System.exit(result ? 0 : 1);
    }
}