reduce个数问题

reduce的数目到底和哪些因素有关

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的大小可以决定单个tasktracker一次性启动reduce的数目，但是不能决定总的reduce数目。

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

只有2个reduce在干活。为什么呢？ 
shuffle的过程，需要根据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中可以设置。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根据这个返回结果决定将这条<K,V>,送到哪一个reduce中去。

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

简简单单的返回了原值的整型值。 
 因为getPartition(K2 key, V2 value,int numReduceTask)返回的结果只有2个不同的值，所以最终只有2个reduce在干活。

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) {

	}
}

仅仅需要覆盖getPartition()方法就OK。通过： 
conf.setPartitionerClass(MyPartitioner.class); 
可以设置自定义的partition类。 
同样由于之返回2个不同的值0，1，不管conf.setNumReduceTasks(4);设置多少个reduce，也同样只会有2个reduce在干活。

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，由于使用了自定义的MyPartitioner，所有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。 
每个reduce的输出key又是经过key排序的，所以最终的结果集降序排列。

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

只启动了一个reduce。 
  (1)、 当setNumReduceTasks( int a) a=1(即默认值),不管Partitioner返回不同值的个数b为多少，只启动1个reduce,这种情况下自定义的Partitioner类没有起到任何作用。 
  (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;
	}

同时设置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没有找到所对应的reduce去处。原因是只启动了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，同样会启动a个reduce，但是只有b个redurce上会得到数据。启动的其他的a-b个reduce浪费了。

c、理想状况是a=b，这样可以合理利用资源，负载更均衡。

总结和map partition函数和参数的设置有关