Hadoop中WritableComparable 和 comparator

1.WritableComparable

查看HadoopAPI,如图所示：

WritableComparable继承自Writable和java.lang.Comparable接口，是一个Writable也是一个Comparable，也就是说，既可以序列化，也可以比较！

再看看它的实现类，发现BooleanWritable, BytesWritable, ByteWritable, DoubleWritable, FloatWritable, IntWritable, LongWritable, MD5Hash, NullWritable, Record, RecordTypeInfo, Text, VIntWritable, VLongWritable都实现了WritableComparable类！

WritableComparable的实现类之间相互来比较，在Map/Reduce中，任何用作键来使用的类都应该实现WritableComparable接口！

 

Example:

package cn.roboson.writable;

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

import org.apache.hadoop.io.WritableComparable;

/**
 * 1.自定义一个类，继承WritableComparable
 * 2.发现有三个未实现的方法，两个是Writable接口的（序列化），一个是Comparable接口的（用来比较）
 * 3.自定义比较，这里以counter来作为比较
 * @author roboson
 *
 */
public class MyWritableComparable implements WritableComparable<MyWritableComparable>{
    
    private int counter;
    private long timestamp;
    public MyWritableComparable() {
        // TODO Auto-generated constructor stub
    }

    public MyWritableComparable(int counter,long timestamp) {
        // TODO Auto-generated constructor stub
        this.counter = counter;
        this.timestamp = timestamp;
    }
    
    @Override
    public void readFields(DataInput in) throws IOException {
        // TODO Auto-generated method stub
        
        //将输入流中的字节流数据转化为结构化数据
        counter = in.readInt();
        timestamp = in.readLong();
    }

    @Override
    public void write(DataOutput out) throws IOException {
        // TODO Auto-generated method stub
        
        //讲结构化数据写入输出流
        out.writeInt(counter);
        out.writeLong(timestamp);
    }

    @Override
    public int compareTo(MyWritableComparable other) {
        // TODO Auto-generated method stub
        int thisValue = this.counter;
        int otherValue = other.counter;
        return (thisValue < otherValue ? -1 : (thisValue == otherValue ? 0 : 1));
    }

    public int getCounter() {
        return counter;
    }

    public void setCounter(int counter) {
        this.counter = counter;
    }

    public long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(long timestamp) {
        this.timestamp = timestamp;
    }
    

    public static void main(String[] args) {
        MyWritableComparable comparable = new MyWritableComparable(3,4);
        MyWritableComparable otherComparable = new MyWritableComparable(4, 5);
        int value = comparable.compareTo(otherComparable);
        if(value==-1){
            System.out.println("comparable<otherComparable");
        }else if(value==0){
            System.out.println("comparable=otherComparable");
        }else{
            System.out.println("comparable>otherComparable");
        }
    }
}

 

运行结果：

 

2.RawComparator

对于MapReduce来说，因为中间有个基于键的排序阶段，所以类型的比较是非常重要的。Hadoop中提供了原生的比较接口RawComparator，该接口继承子Java Comparator接口。RawComparator接口允许其实现直接比较数据流中的记录，无需先把数据流饭序列化为对象，这样便避免了新建对象的额外开销。

package org.apache.hadoop.io;

import java.util.Comparator;

public interface RawComparator<T> extends Comparator<T>{
    
    //自己的方法
    public int compare(byte[] b1, int s1, int l1, byte[] b2,int s2, int l2);
    
    //继承自Comparator的方法
    @Override
    public int compare(T o1, T o2);
    
    @Override
    public boolean equals(Object obj);
}

 

查看HadoopAPI：

该类并非被多数的衍生类所实现，其具体的子类为WritableComparator，多数情况下是作为实现Writable接口的类的内置类，提供序列化字节的比较。如下图说所示：BooleanWritable, BytesWritable, ByteWritable, org.apache.hadoop.io.serializer.DeserializerComparator, DoubleWritable, FloatWritable, IntWritable, JavaSerializationComparator, LongWritable, LongWritable, MD5Hash, NullWritable， RecordComparator, Text, UTF8,都实现了RawComparator，作为其内部类。

而WritableComparator则是其的具体子类。

3.WritableComparator

在《Hadoop权威指南》中，说到这儿，很模糊，只说WritableComparator是对继承自WritableComparable类的RawCompartor类的一个通用实现。让人看着很迷惑，这句话什么意思呢？

首先、在第二个小标题RawComparator中，我门都知道WritableComparator实现了RawComparator这个接口，也就是说，WritableComparator是RawComparator的实现。

其次、是对继承自WritableComparable类的RawComparator的一个通用实现。那么继承自WritableComparable类的RawComparator都有哪些呢？也就是说那些类，继承自WritableComparator，并且实现了RawComparator？在第二个小标题RawComparator中有也都说明清楚了，上面的红色部分!同理，实现了WritableComparable类的在第一个小标题WritableComparable中也有说明，红色部分字体！也就谁说WritableComparator是对BooleanWritable.Comparator, BytesWritable.Comparator, ByteWritable.Comparator, DoubleWritable.Comparator, FloatWritable.Comparator, IntWritable.Comparator, LongWritable.Comparator, MD5Hash.Comparator, NullWritable.Comparator, RecordComparator, Text.Comparator, UTF8.Comparator这些类的一个通用实现！这句话就引出了WritableComparator的两个功能：第一，它提供了对原始compare()方法的一个默认实现。该方法能够饭序列化将流中进行比较的对象，并调用对象的compara()方法。第二，它充当的是RawComparator实例的工厂（已注册Writable的实现）。例如，为了获得IntWratable的comparator，我们直接如下调用：

RawComparator<IntWritable> comparator = WritableComparator.get(IntWratable.class);

再来看看WritableComparator这个类是如何定义的，如下图所示：

WritableComparator类类似于一个注册表，里面记录了所有Comparator类的集合。Comparators成员用一张Hash表记录Key=Class，value=WritableComprator的注册信息.这就是它能够充当RawComparator实例工厂的原因!因为它本省的实现中有意个HashMap集合，HashMap<Class,WritableComparator>根据对应的Class，就能返回一个响应的WritableComparator!

 

Example:

package cn.roboson.writable;

import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.RawComparator;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparator;

/**
 * 1.通过WritableComparator获得IntWritable类的RawComparator实例
 * 2.通过两种方式来比较
 * @author roboson
 *
 */

public class ComparableFinish {
    
    public static void main(String[] args) throws IOException {
        
        //创建两个IntWritable来比较
        IntWritable writable1 = new IntWritable(163);
        IntWritable writable2 = new IntWritable(165);
        
        //获得IntWritable的RawComparator实例
        RawComparator<IntWritable> intRawComparator = WritableComparator.get(IntWritable.class);
        
        //直接比较对象
        int value1 =intRawComparator.compare(writable1, writable2);
        
        if(value1==-1){
            System.out.println("writable1<writable2");
        }else if(value1==0){
            System.out.println("writable1=writable2");
        }else{
            System.out.println("writable1>writable2");
        }
        
        //序列化两个对象，获得其字节流
        byte[] byte1 = serizlize(writable1);
        byte[] byte2 = serizlize(writable2);
        
        //直接通过字符流比较大小
        int value2 = intRawComparator.compare(byte1, 0, 4, byte2, 0, 4);
        if(value2==-1){
            System.out.println("writable1<writable2");
        }else if(value2==0){
            System.out.println("writable1=writable2");
        }else{
            System.out.println("writable1>writable2");
        }
    }
    
    public static byte[] serizlize(Writable writable) throws IOException{
        
        //创建一个输出字节流对象
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        DataOutputStream dataout = new DataOutputStream(out);
        
        //将结构化数据的对象writable写入到输出字节流。
        writable.write(dataout);
        return out.toByteArray();
    }
    
    public static byte[] deserizlize(Writable writable,byte[] bytes) throws IOException{
        
        //创建一个输入字节流对象，将字节数组中的数据，写入到输入流中
        ByteArrayInputStream in = new ByteArrayInputStream(bytes);
        DataInputStream datain = new DataInputStream(in);
        
        //将输入流中的字节流数据反序列化
        writable.readFields(datain);
        return bytes;
        
    }
}

 

运行结果：

关于序列化方面的知识，可以参考我的博客《Hadoop序列化》地址如下：

http://www.cnblogs.com/robert-blue/p/4157768.html

参考博文：

http://blog.csdn.net/keda8997110/article/details/8518255

http://www.360doc.com/content/12/0827/09/9318309_232551844.shtml