MapReduce程序之序列化原理与Writable案例

发布时间：2020-07-31 05:54:46 作者：xpleaf
来源：网络阅读：4062

[TOC]

MapReduce程序之序列化原理与Writable案例

前言

在编写MapReduce程序时，我们会发现，对于MapReduce的输入输出数据（key-value），我们只能使用Hadoop提供的数据类型，而不能使用Java本身的基本数据类型，比如，如果数据类型为long，那么在编写MR程序时，对应Hadoop的数据类型则为LongWritable。关于原因，简单说明如下：

hadoop在节点间的内部通讯使用的是RPC，RPC协议把消息翻译成二进制字节流发送到远程节点，
远程节点再通过反序列化把二进制流转成原始的信息。也就是说，传递的消息内容是需要经过hadoop特定的序列化与反序列化操作的，因此，才需要使用hadoop提供的数据类型，当然，如果想要自定义MR程序中key-value的数据类型，则需要实现相应的接口，如Writable、WritableComparable接口。

也就是说，如果需要自定义key-value的数据类型，必须要遵循如下的原则：

/**
 * MapReduce的任意的key和value都必须要实现Writable接口
 * MapReduce的任意key必须实现WritableComparable接口，WritableComparable是Writable的增强版
 * key还需要实现Comparable的原因在于，对key排序是MapReduce模型中的基本功能
 */

其实前面写的很多Netty的文章，到了后面写编解码技术时，需要实现的功能与Hadoop是一样的，因为到最后的目的，我也是希望自己写一个RPC框架（模仿阿里的dubbo）。

Writable接口

关于Writable接口，源代码中的解释就非常好了：

/**
 * A serializable object which implements a simple, efficient, serialization 
 * protocol, based on {@link DataInput} and {@link DataOutput}.
 *
 * <p>Any <code>key</code> or <code>value</code> type in the Hadoop Map-Reduce
 * framework implements this interface.</p>
 * 
 * <p>Implementations typically implement a static <code>read(DataInput)</code>
 * method which constructs a new instance, calls {@link #readFields(DataInput)} 
 * and returns the instance.</p>
 * 
 * <p>Example:</p>
 * <p><blockquote><pre>
 *     public class MyWritable implements Writable {
 *       // Some data     
 *       private int counter;
 *       private long timestamp;
 *       
 *       public void write(DataOutput out) throws IOException {
 *         out.writeInt(counter);
 *         out.writeLong(timestamp);
 *       }
 *       
 *       public void readFields(DataInput in) throws IOException {
 *         counter = in.readInt();
 *         timestamp = in.readLong();
 *       }
 *       
 *       public static MyWritable read(DataInput in) throws IOException {
 *         MyWritable w = new MyWritable();
 *         w.readFields(in);
 *         return w;
 *       }
 *     }
 * </pre></blockquote></p>
 */

WritableComparable接口

直接给出官方源码中的解释：


/**
 * A {@link Writable} which is also {@link Comparable}. 
 *
 * <p><code>WritableComparable</code>s can be compared to each other, typically 
 * via <code>Comparator</code>s. Any type which is to be used as a 
 * <code>key</code> in the Hadoop Map-Reduce framework should implement this
 * interface.</p>
 *
 * <p>Note that <code>hashCode()</code> is frequently used in Hadoop to partition
 * keys. It's important that your implementation of hashCode() returns the same 
 * result across different instances of the JVM. Note also that the default 
 * <code>hashCode()</code> implementation in <code>Object</code> does <b>not</b>
 * satisfy this property.</p>
 *  
 * <p>Example:</p>
 * <p><blockquote><pre>
 *     public class MyWritableComparable implements WritableComparable<MyWritableComparable> {
 *       // Some data
 *       private int counter;
 *       private long timestamp;
 *       
 *       public void write(DataOutput out) throws IOException {
 *         out.writeInt(counter);
 *         out.writeLong(timestamp);
 *       }
 *       
 *       public void readFields(DataInput in) throws IOException {
 *         counter = in.readInt();
 *         timestamp = in.readLong();
 *       }
 *       
 *       public int compareTo(MyWritableComparable o) {
 *         int thisValue = this.value;
 *         int thatValue = o.value;
 *         return (thisValue < thatValue ? -1 : (thisValue==thatValue ? 0 : 1));
 *       }
 *
 *       public int hashCode() {
 *         final int prime = 31;
 *         int result = 1;
 *         result = prime * result + counter;
 *         result = prime * result + (int) (timestamp ^ (timestamp >>> 32));
 *         return result
 *       }
 *     }
 * </pre></blockquote></p>
 */

Writable接口案例

下图是电信一段日志记录的表结构，现需要统计每一个手机号码的upPackNum、downPackNum、upPayLoad、downPayLoad的总和。
MapReduce程序之序列化原理与Writable案例

要求：使用自定义Writable完成。

数据准备

提供的文本数据如下：

1363157985066,13726230503,00-FD-07-A4-72-B8:CMCC,120.196.100.82,i02.c.aliimg.com,,24,27,2481,24681,200
1363157995052,13826544101,5C-0E-8B-C7-F1-E0:CMCC,120.197.40.4,,,4,0,264,0,200
1363157991076,13926435656,20-10-7A-28-CC-0A:CMCC,120.196.100.99,,,2,4,132,1512,200
1363154400022,13926251106,5C-0E-8B-8B-B1-50:CMCC,120.197.40.4,,,4,0,240,0,200
1363157993044,18211575961,94-71-AC-CD-E6-18:CMCC-EASY,120.196.100.99,iface.qiyi.com,视频网站,15,12,1527,2106,200
1363157995074,84138413,5C-0E-8B-8C-E8-20:7DaysInn,120.197.40.4,122.72.52.12,,20,16,4116,1432,200
1363157993055,13560439658,C4-17-FE-BA-DE-D9:CMCC,120.196.100.99,,,18,15,1116,954,200
1363157995033,15920133257,5C-0E-8B-C7-BA-20:CMCC,120.197.40.4,sug.so.360.cn,信息安全,20,20,3156,2936,200
1363157983019,13719199419,68-A1-B7-03-07-B1:CMCC-EASY,120.196.100.82,,,4,0,240,0,200
1363157984041,13660577991,5C-0E-8B-92-5C-20:CMCC-EASY,120.197.40.4,s19.cnzz.com,站点统计,24,9,6960,690,200
1363157973098,15013685858,5C-0E-8B-C7-F7-90:CMCC,120.197.40.4,rank.ie.sogou.com,搜索引擎,28,27,3659,3538,200
1363157986029,15989002119,E8-99-C4-4E-93-E0:CMCC-EASY,120.196.100.99,www.umeng.com,站点统计,3,3,1938,180,200
1363157992093,13560439658,C4-17-FE-BA-DE-D9:CMCC,120.196.100.99,,,15,9,918,4938,200
1363157986041,13480253104,5C-0E-8B-C7-FC-80:CMCC-EASY,120.197.40.4,,,3,3,180,180,200
1363157984040,13602846565,5C-0E-8B-8B-B6-00:CMCC,120.197.40.4,2052.flash3-http.qq.com,综合门户,15,12,1938,2910,200
1363157995093,13922314466,00-FD-07-A2-EC-BA:CMCC,120.196.100.82,img.qfc.cn,,12,12,3008,3720,200
1363157982040,13502468823,5C-0A-5B-6A-0B-D4:CMCC-EASY,120.196.100.99,y0.ifengimg.com,综合门户,57,102,7335,110349,200
1363157986072,18320173382,84-25-DB-4F-10-1A:CMCC-EASY,120.196.100.99,input.shouji.sogou.com,搜索引擎,21,18,9531,2412,200
1363157990043,13925057413,00-1F-64-E1-E6-9A:CMCC,120.196.100.55,t3.baidu.com,搜索引擎,69,63,11058,48243,200
1363157988072,13760778710,00-FD-07-A4-7B-08:CMCC,120.196.100.82,,,2,2,120,120,200
1363157985079,13823070001,20-7C-8F-70-68-1F:CMCC,120.196.100.99,,,6,3,360,180,200
1363157985069,13600217502,00-1F-64-E2-E8-B1:CMCC,120.196.100.55,,,18,138,1080,186852,200

实现Writable接口的HttpDataWritable

下面就基于Writable接口写一个HttpDataWritable类，代码如下：

package com.uplooking.bigdata.mr.http;

import org.apache.hadoop.io.Writable;

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

/**
 * MapReduce的任意的key和value都必须要实现Writable接口
 * MapReduce的任意key必须实现WritableComparable接口，WritableComparable是Writable的增强版
 */
public class HttpDataWritable implements Writable {

    private long upPackNum;
    private long downPackNum;
    private long upPayLoad;
    private long downPayLoad;

    public HttpDataWritable() {

    }

    public HttpDataWritable(long upPackNum, long downPackNum, long upPayLoad, long downPayLoad) {
        this.upPackNum = upPackNum;
        this.downPackNum = downPackNum;
        this.upPayLoad = upPayLoad;
        this.downPayLoad = downPayLoad;
    }

    public void write(DataOutput out) throws IOException {
        out.writeLong(upPackNum);
        out.writeLong(downPackNum);
        out.writeLong(upPayLoad);
        out.writeLong(downPayLoad);
    }

    public void readFields(DataInput in) throws IOException {
        this.upPackNum = in.readLong();
        this.downPackNum = in.readLong();
        this.upPayLoad = in.readLong();
        this.downPayLoad = in.readLong();
    }

    public long getUpPackNum() {
        return upPackNum;
    }

    public void setUpPackNum(long upPackNum) {
        this.upPackNum = upPackNum;
    }

    public long getDownPackNum() {
        return downPackNum;
    }

    public void setDownPackNum(long downPackNum) {
        this.downPackNum = downPackNum;
    }

    public long getUpPayLoad() {
        return upPayLoad;
    }

    public void setUpPayLoad(long upPayLoad) {
        this.upPayLoad = upPayLoad;
    }

    public long getDownPayLoad() {
        return downPayLoad;
    }

    public void setDownPayLoad(long downPayLoad) {
        this.downPayLoad = downPayLoad;
    }

    @Override
    public String toString() {
        return upPackNum + "\t" + downPackNum + "\t" +
                upPayLoad + "\t" + downPayLoad;
    }
}

MapReduce程序

程序代码如下：

package com.uplooking.bigdata.mr.http;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import java.io.IOException;

public class HttpDataJob {

    public static void main(String[] args) throws Exception {
        if (args == null || args.length < 2) {
            System.err.println("Parameter Errors! Usages:<inputpath> <outputpath>");
            System.exit(-1);
        }

        Path inputPath = new Path(args[0]);
        Path outputPath = new Path(args[1]);

        Configuration conf = new Configuration();
        String jobName = HttpDataJob.class.getSimpleName();
        Job job = Job.getInstance(conf, jobName);
        //设置job运行的jar
        job.setJarByClass(HttpDataJob.class);
        //设置整个程序的输入
        FileInputFormat.setInputPaths(job, inputPath);
        job.setInputFormatClass(TextInputFormat.class);//就是设置如何将输入文件解析成一行一行内容的解析类
        //设置mapper
        job.setMapperClass(HttpDataMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(HttpDataWritable.class);
        //设置整个程序的输出
        // outputpath.getFileSystem(conf).delete(outputpath, true);//如果当前输出目录存在，删除之，以避免.FileAlreadyExistsException
        FileOutputFormat.setOutputPath(job, outputPath);
        job.setOutputFormatClass(TextOutputFormat.class);
        //设置reducer
        job.setReducerClass(HttpDataReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(HttpDataWritable.class);

        //指定程序有几个reducer去运行
        job.setNumReduceTasks(1);
        //提交程序
        job.waitForCompletion(true);
    }

    public static class HttpDataMapper extends Mapper<LongWritable, Text, Text, HttpDataWritable> {
        @Override
        protected void map(LongWritable k1, Text v1, Context context) throws IOException, InterruptedException {
            String line = v1.toString();
            String[] items = line.split(",");
            // 获取手机号码
            String phoneNum = items[1];
            // 获取upPackNum、downPackNum、upPayLoad、downPayLoad
            long upPackNum = Long.parseLong(items[6]);
            long downPackNum = Long.parseLong(items[7]);
            long upPayLoad = Long.parseLong(items[8]);
            long downPayLoad = Long.parseLong(items[9]);
            // 构建HttpDataWritable对象
            HttpDataWritable httpData = new HttpDataWritable(upPackNum, downPackNum, upPayLoad, downPayLoad);
            // 写出数据到context
            context.write(new Text(phoneNum), httpData);
        }
    }

    public static class HttpDataReducer extends Reducer<Text, HttpDataWritable, Text, HttpDataWritable> {
        @Override
        protected void reduce(Text k2, Iterable<HttpDataWritable> v2s, Context context) throws IOException, InterruptedException {
            long upPackNum = 0L;
            long downPackNum = 0L;
            long upPayLoad = 0L;
            long downPayLoad = 0L;
            // 遍历v2s，计算各个参数的总和
            for(HttpDataWritable htd : v2s) {
                upPackNum += htd.getUpPackNum();
                downPackNum += htd.getDownPackNum();
                upPayLoad += htd.getUpPayLoad();
                downPayLoad += htd.getDownPayLoad();
            }
            // 构建HttpDataWritable对象
            HttpDataWritable httpData = new HttpDataWritable(upPackNum, downPackNum, upPayLoad, downPayLoad);
            // 写出数据到context
            context.write(k2, httpData);
        }
    }
}

测试

注意，上面的程序是需要读取命令行的参数输入的，可以在本地的环境执行，也可以打包成一个jar包上传到Hadoop环境的Linux服务器上执行，这里，我使用的是本地环境（我的操作系统是Mac OS），输入的参数如下：

/Users/yeyonghao/data/input/HTTP_20160415143750.dat /Users/yeyonghao/data/output/mr/http/h-1

运行程序后，查看输出结果，如下：

yeyonghao@yeyonghaodeMacBook-Pro:~/data/output/mr/http/h-1$ cat part-r-00000
13480253104 3   3   180 180
13502468823 57  102 7335    110349
13560439658 33  24  2034    5892
13600217502 18  138 1080    186852
13602846565 15  12  1938    2910
13660577991 24  9   6960    690
13719199419 4   0   240 0
13726230503 24  27  2481    24681
13760778710 2   2   120 120
13823070001 6   3   360 180
13826544101 4   0   264 0
13922314466 12  12  3008    3720
13925057413 69  63  11058   48243
13926251106 4   0   240 0
13926435656 2   4   132 1512
15013685858 28  27  3659    3538
15920133257 20  20  3156    2936
15989002119 3   3   1938    180
18211575961 15  12  1527    2106
18320173382 21  18  9531    2412
84138413    20  16  4116    1432

说明我们的MapReduce程序没有问题，并且写的HttpDataWritable类也是可以正常使用的。

MapReduce程序之序列化原理与Writable案例