reduce个数究竟和哪些因素有关

reduce的数目究竟和哪些因素有关

1、我们知道map的数量和文件数、文件大小、块大小、以及split大小有关，而reduce的数量跟哪些因素有关呢？



 设置mapred.tasktracker.reduce.tasks.maximum的大小能够决定单个tasktracker一次性启动reduce的数目，可是不能决定总的reduce数目。

conf.setNumReduceTasks(4);JobConf对象的这种方法能够用来设定总的reduce的数目，看下Job Counters的统计：

	Job Counters
		Data-local map tasks=2
		Total time spent by all maps waiting after reserving slots (ms)=0
		Total time spent by all reduces waiting after reserving slots (ms)=0
		SLOTS_MILLIS_MAPS=10695
		SLOTS_MILLIS_REDUCES=29502
		Launched map tasks=2
		Launched reduce tasks=4

确实启动了4个reduce：看下输出：

diegoball@diegoball:~/IdeaProjects/test/build/classes$ hadoop fs -ls  /user/diegoball/join_ou1123
11/03/25 15:28:45 INFO security.Groups: Group mapping impl=org.apache.hadoop.security.ShellBasedUnixGroupsMapping; cacheTimeout=300000
11/03/25 15:28:45 WARN conf.Configuration: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
Found 5 items
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:28 /user/diegoball/join_ou1123/_SUCCESS
-rw-r--r--   1 diegoball supergroup        124 2011-03-25 15:27 /user/diegoball/join_ou1123/part-00000
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:27 /user/diegoball/join_ou1123/part-00001
-rw-r--r--   1 diegoball supergroup        214 2011-03-25 15:28 /user/diegoball/join_ou1123/part-00002
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:28 /user/diegoball/join_ou1123/part-00003

仅仅有2个reduce在干活。为什么呢？



shuffle的过程。须要依据key的值决定将这条<K,V> （map的输出）,送到哪一个reduce中去。送到哪一个reduce中去靠调用默认的org.apache.hadoop.mapred.lib.HashPartitioner的getPartition()方法来实现。

HashPartitioner类：

package org.apache.hadoop.mapred.lib;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.mapred.Partitioner;
import org.apache.hadoop.mapred.JobConf;

/** Partition keys by their {@link Object#hashCode()}.
 */
@InterfaceAudience.Public
@InterfaceStability.Stable
public class HashPartitioner<K2, V2> implements Partitioner<K2, V2> {

  public void configure(JobConf job) {}

  /** Use {@link Object#hashCode()} to partition. */
  public int getPartition(K2 key, V2 value,
                          int numReduceTasks) {
    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
  }
}

numReduceTasks的值在JobConf中能够设置。

默认的是1：显然太小。

这也是为什么默认的设置中总启动一个reduce的原因。



   返回与运算的结果和numReduceTasks求余。



   Mapreduce依据这个返回结果决定将这条<K,V>,送到哪一个reduce中去。

key传入的是LongWritable类型，看下这个LongWritable类的hashcode()方法：

 public int hashCode() {
    return (int)value;
  }

简简单单的返回了原值的整型值。



 由于getPartition(K2 key, V2 value,int numReduceTask)返回的结果仅仅有2个不同的值，所以终于仅仅有2个reduce在干活。

 



 HashPartitioner是默认的partition类。我们也能够自己定义partition类 ：

 package com.alipay.dw.test;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.Partitioner;

/**
 * Created by IntelliJ IDEA.
 * User: diegoball
 * Date: 11-3-10
 * Time: 下午5:26
 * To change this template use File | Settings | File Templates.
 */
public class MyPartitioner implements Partitioner<IntWritable, IntWritable> {
    public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
        /* Pretty ugly hard coded partitioning function. Don't do that in practice, it is just for the sake of understanding. */
        int nbOccurences = key.get();
        if (nbOccurences > 20051210)
            return 0;
        else
            return 1;
    }

    public void configure(JobConf arg0) {

    }
}

只须要覆盖getPartition()方法就OK。

通过：

conf.setPartitionerClass(MyPartitioner.class);

能够设置自己定义的partition类。

相同因为之返回2个不同的值0，1，无论conf.setNumReduceTasks(4);设置多少个reduce，也相同仅仅会有2个reduce在干活。



因为每一个reduce的输出key都是经过排序的，上述自己定义的Partitioner还能够达到排序结果集的目的：

11/03/25 15:24:49 WARN conf.Configuration: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
Found 5 items
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:23 /user/diegoball/opt.del/_SUCCESS
-rw-r--r--   1 diegoball supergroup      24546 2011-03-25 15:23 /user/diegoball/opt.del/part-00000
-rw-r--r--   1 diegoball supergroup      10241 2011-03-25 15:23 /user/diegoball/opt.del/part-00001
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:23 /user/diegoball/opt.del/part-00002
-rw-r--r--   1 diegoball supergroup          0 2011-03-25 15:23 /user/diegoball/opt.del/part-00003

part-00000和part-00001是这2个reduce的输出，因为使用了自己定义的MyPartitioner，全部key小于20051210的的<K，V>都会放到第一个reduce中处理。key大于20051210就会被放到第二个reduce中处理。

每一个reduce的输出key又是经过key排序的，所以终于的结果集降序排列。





可是假设使用上面自己定义的partition类，又conf.setNumReduceTasks(1)的话。会如何？ 看下Job Counters：

	Job Counters
		Data-local map tasks=2
		Total time spent by all maps waiting after reserving slots (ms)=0
		Total time spent by all reduces waiting after reserving slots (ms)=0
		SLOTS_MILLIS_MAPS=16395
		SLOTS_MILLIS_REDUCES=3512
		Launched map tasks=2
		Launched reduce tasks=1

仅仅启动了一个reduce。

  (1)、 当setNumReduceTasks( int a) a=1(即默认值),无论Partitioner返回不同值的个数b为多少，仅仅启动1个reduce,这样的情况下自己定义的Partitioner类没有起到不论什么作用。

(2)、 若a!=1:

   a、当setNumReduceTasks( int a)里 a设置小于Partitioner返回不同值的个数b的话：

    public int getPartition(IntWritable key, IntWritable value, int numPartitions) {
        /* Pretty ugly hard coded partitioning function. Don't do that in practice, it is just for the sake of understanding. */
        int nbOccurences = key.get();
        if (nbOccurences < 20051210)
            return 0;
        if (nbOccurences >= 20051210 && nbOccurences < 20061210)
            return 1;
        if (nbOccurences >= 20061210 && nbOccurences < 20081210)
            return 2;
        else
            return 3;
    }

同一时候设置setNumReduceTasks( 2)。

于是抛出异常：

  11/03/25 17:03:41 INFO mapreduce.Job: Task Id : attempt_201103241018_0023_m_000000_1, Status : FAILED
java.io.IOException: Illegal partition for 20110116 (3)
	at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:900)
	at org.apache.hadoop.mapred.MapTask$OldOutputCollector.collect(MapTask.java:508)
	at com.alipay.dw.test.KpiMapper.map(Unknown Source)
	at com.alipay.dw.test.KpiMapper.map(Unknown Source)
	at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:54)
	at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:397)
	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:330)
	at org.apache.hadoop.mapred.Child$4.run(Child.java:217)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:396)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:742)
	at org.apache.hadoop.mapred.Child.main(Child.java:211)

某些key没有找到所相应的reduce去处。

原因是仅仅启动了a个reduce。

 

   b、当setNumReduceTasks( int a)里 a设置大于Partitioner返回不同值的个数b的话，相同会启动a个reduce。可是仅仅有b个redurce上会得到数据。启动的其它的a-b个reduce浪费了。

c、理想状况是a=b，这样能够合理利用资源，负载更均衡。