MapReduce-序列化（Writable）

Hadoop 序列化特点

Java 的序列化是一个重量级序列化框架（Serializable），一个对象被序列化后，会附带很多额外的信息（各种校验信息，Header，继承体系等），不便于在网络中高效传输。所以，Hadoop 自己开发了一套序列化机制（Writable）

Hadoop 序列化特点：
紧凑：高效使用存储空间
快速：读写数据的额外开销小
可扩展：随着通信协议的升级而可升级
互操作：支持多语言的交互

常用数据类型对应的 Hadoop 数据序列化类型

Java类型	Hadoop Writable类型
boolean	BooleanWritable
byte	ByteWritable
int	IntWritable
float	FloatWritable
long	LongWritable
double	DoubleWritable
String	Text
map	MapWritable
array	ArrayWritable

自定义序列化数据类型

（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
（3）重写序列化方法
（4）重写反序列化方法
（5）注意反序列化的顺序和序列化的顺序完全一致
（6）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
（7）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

测试：完成手机号的总上行流量，总下行流量，总流量的统计

测试数据 phone.txt

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 	13470253144	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

定义序列化对象

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

import org.apache.hadoop.io.Writable;

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;

    public FlowBean() {
        // 空参构造， 后续反射用
        super();
    }

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

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

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

    @Override
    public String toString() {
        return upFlow + "\t" + downFlow + "\t" + sumFlow;
    }

    public long getSumFlow() {
        return sumFlow;
    }

    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }
}

MapReduce程序

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.log4j.BasicConfigurator;

import java.io.IOException;

public class FlowsumDriver {

    static {
        try {
            // 设置 HADOOP_HOME 环境变量
            System.setProperty("hadoop.home.dir", "D://DevelopTools/hadoop-2.9.2/");
            // 日志初始化
            BasicConfigurator.configure();
            // 加载库文件
            System.load("D://DevelopTools/hadoop-2.9.2/bin/hadoop.dll");
        } catch (UnsatisfiedLinkError e) {
            System.err.println("Native code library failed to load.\n" + e);
            System.exit(1);
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        // 获取job对象
        Job job = Job.getInstance(conf);

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

        // 关联mapper和reducer
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

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

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

        // 设置输入输出路径
        args = new String[]{"D://tmp/phone.txt", "D://tmp/456"};
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 提交job
        boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 : 1);
    }
}

class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> {

    private Text k = new Text();
    private FlowBean v;

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 获取一行
        String line = value.toString();
        // 切割 \t
        String[] fields = line.split("\t");
        // 手机号
        k.set(fields[1]);
        long upFlow = Long.parseLong(fields[fields.length - 3]);
        long downFlow = Long.parseLong(fields[fields.length - 2]);
        v = new FlowBean(upFlow, downFlow);
        // 写出
        context.write(k, v);
    }
}

class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {

    private FlowBean v;

    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
        long sumUpFlow = 0L;
        long sumDownFlow = 0L;
        // 累加求和
        for (FlowBean flowBean : values) {
            sumUpFlow += flowBean.getUpFlow();
            sumDownFlow += flowBean.getDownFlow();
        }
        v = new FlowBean(sumUpFlow, sumDownFlow);
        // 写出
        context.write(key, v);
    }
}

结果 part-r-00000

13470253144	4296	1612	5908
13509468723	7335	110349	117684
13560439638	918	4938	5856
13568436656	3597	25635	29232
13590439668	1116	954	2070
13630577991	6960	690	7650
13682846555	1938	2910	4848
13729199489	240	0	240
13736230513	2481	24681	27162
13768778790	120	120	240
13846544121	264	0	264
13956435636	132	1512	1644
13966251146	240	0	240
13975057813	11058	48243	59301
13992314666	3008	3720	6728
15043685818	3659	3538	7197
15910133277	3156	2936	6092
15959002129	1938	180	2118
18271575951	1527	2106	3633
18390173782	9531	2412	11943

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http://hadoop.apache.org/docs/current/api/org/apache/hadoop/io/Writable.html