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。
conf.setNumReduceTasks(4);JobConf对象的这个方法可以用来设定总的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
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。
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。
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。
HashPartitioner是默认的partition类，我们也可以自定义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) { } }
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。
由于每个reduce的输出key都是经过排序的，上述自定义的Partitioner还可以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，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。
但是如果使用上面自定义的partition类，又conf.setNumReduceTasks(1)的话，会怎样？ 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
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。
