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hadoop wangting 1年前 (2019-05-14) 3811次浏览 66个评论

我在15年处理大数据的时候还都是使用MapReduce, 随着时间的推移, 计算工具的发展, 内存越来越便宜, 计算方式也有了极大的改变. 到现在再做大数据开发的好多同学都是直接使用spark, hive等工具, 很少有再写MapReduce的了.
这里整理一下MapReduce中经常用到的二次排序的方法, 全当复习.


二次排序(secondary sort)问题是指在Reduce阶段对某个键关联的值排序. 利用二次排序技术,可以对传入Reduce的值完成 升序/降序 排序.
MapReduce框架会自动对Map生成的完成排序. 所以, 在启动Reduce之前,中间文件 key-value 是按照key有序的(而不是按照值有序). 它们的值得顺序有可能是任意的.


对Reduce中的值排序至少有两种方案, 这两种方案在MapReduce/HadoopSpark框架中都可以使用.

  • 第一种方案是让Reduce读取和缓存给定key的所有的value, 然后在Reduce中对这些值完成排序.(例如: 把一个key对应的所有value放到一个ArrayList中,再排序). 但是这种方式有局限性, 如果数据量较少还可以使用,如果数据量太大,一个Reduce中放不下所有的值,就会导致内存溢出(OutOfMemory).
  • 第二种方式是使用MapReduce框架来对值进行排序. 因为MapReduce框架会自动对Map生成的文件的key进行排序, 所以我们把需要排序的value增加到这个key上,这样让框架对这个new_key进行排序,来实现我们的目标.


  1. 使用值键转换设计模式:构造一个组合的中间key,new_key(k, v1), 其中v1是次键(secondary key).
  2. MapReduce执行框架完成排序.
  3. 重写分区器,使组合键(k, v1) 按照之前单独的 k 进行分区.


有4列分别为: 年, 月, 日, 温度.





要实现二次排序的特性,还需要一些java的插件类, 去告诉MapReduce框架一些信息:

  • 如何对Reduce的键排序.
  • 如何对Map产出的数据进行分区,进到不同的Reduce.
  • 如何对Reduce中的数据进行分组.


要实现二次排序, 我们需要控制组合键的排序顺序,以及Reduce处理键的顺序.
首先组合键的组成由(年-月 + 温度)一起组成, 如下图:

temperature的数据放到键中之后, 我们还要指定这个组合键排序方式. 使用DateTemperaturePair对象保存组合键, 重写其compareTo()方法指定排序顺序.
Hadoop中,如果需要持久存储定制数据类型(如DateTemperaturePair),必须实现Writable接口. 如果要比较定制数据类型, 他们还必须实现另外一个接口WritableComparable. 示例代码如下:

import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
public class DateTemperaturePair implements Writable, WritableComparable<DateTemperaturePair> {
    private Text yearMonth = new Text(); //自然键
    private Text day = new Text();
    private IntWritable temperature = new IntWritable(); // 次键
    * 这个比较器将控制键的排序顺序
    * /
    public int compareTo(DateTemperaturePair pair) {
        int compareValue = this.yearMonth.compareTo(pair.getYearMonth());
        if (compareValue == 0) {
            compareValue = temperature.compareTo(pair.getTemperature());
        return compareValue; //升序排序
        //return -1 * compareValue; //降序排序


在这里,我们需要根据yearMonth来分区把数据入到不同的Reduce中, 但是我们的键已经变成了(yearMonth + temperature)的组合了. 所以需要定制分区器来根据yearMonth进行数据分区,把相同的yearMonth入到一个Reduce中. 代码如下:

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
public class DateTemperaturePartitioner extends Partitioner<DateTemperaturePair, Text> {
    public int getPartition(DatetemperaturePair pair, Text text, int numberOfPartitions) {
    return math.abs(pair.getYearMonth().hashCode() % numberOfPartitions);

Hadoop提供了一个插件体系,允许在框架中注入定制分区器代码. 我们在驱动累中完成这个工作,如下:

import org.apache.hadoop.mapreduce.Job;
Job job = ...;


默认是按照key分配, 这里我们期望的是按照组合key(yearMonth + temperature) 中的yearMonth分配, 所以需要重写分组方法.

import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
public class DateTemperatureGroupingComparator extends WritableComparator {
    public DateTemperatureGroupingComparator() {
        super(DateTemperaturePair.class, true);
    * 比较器控制哪些键要分组到一个reduce()方法调用
    public int compare(WritableComparable wc1, WritableComparable wc2) {
        DateTemperaturePair pair = (DateTemperaturePair) wc1;
        DateTemperaturePair pair2 = (DateTemperaturePair) wc12;
        return pair.getYearMonth().compareTo(pair2.getYearMonth());





我们需要排序的时候使用key+value所以我们把key变成了新key, (firstkey, secondkey) 对应为(yearMonth, 温度) .

但是又不想在分区 和 分组的时候使用新key, 所以自己写了Partitioner 和 GroupingComparator 来指定使用组合key中的firstkey来分区,分组.

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