Hadoop内置Partitioner

MapReduce的使用者通常会指定Reduce任务和Reduce任务输出文件的数量（R）。用户在中间key上使用分区函数来对数据进行分区，之后在输入到后续任务执行进程。一个默认的分区函数式使用hash方法（比如常见的：hash(key) mod R）进行分区。hash方法能够产生非常平衡的分区，鉴于此，Hadoop中自带了一个默认的分区类HashPartitioner，它继承了Partitioner类，提供了一个getPartition的方法，它的定义如下所示：
代码：

/** Partition keys by their {@link Object#hashCode()}. */
public class HashPartitioner<K, V> extends Partitioner<K, V> {
  /** Use {@link Object#hashCode()} to partition. */
  public int getPartition(K key, V value,
                          int numReduceTasks) {
    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
  }

}

HashPartitoner所做的事情，其关键代码就一句：(key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;

image.png

自己定制Partitioner

要求实现功能：
要求将手机号码按地区区分，分成五个区
示例：
有一些手机号，和流量使用情况：

1363157985066   13726230503 00-FD-07-A4-72-B8:CMCC  120.196.100.82  i02.c.aliimg.com        24  27  2481    24681   200
1363157995052   13826544101 5C-0E-8B-C7-F1-E0:CMCC  120.197.40.4            4   0   264 0   200
1363157991076   13926435656 20-10-7A-28-CC-0A:CMCC  120.196.100.99          2   4   132 1512    200
1363154400022   13926251106 5C-0E-8B-8B-B1-50:CMCC  120.197.40.4            4   0   240 0   200
1363157993044   18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99  iface.qiyi.com  视频网站    15  12  1527    2106    200
1363157995074   84138413    5C-0E-8B-8C-E8-20:7DaysInn  120.197.40.4    122.72.52.12        20  16  4116    1432    200
1363157993055   13560439658 C4-17-FE-BA-DE-D9:CMCC  120.196.100.99          18  15  1116    954 200
1363157995033   15920133257 5C-0E-8B-C7-BA-20:CMCC  120.197.40.4    sug.so.360.cn   信息安全    20  20  3156    2936    200
1363157983019   13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82          4   0   240 0   200
1363157984041   13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4    s19.cnzz.com    站点统计    24  9   6960    690 200
1363157973098   15013685858 5C-0E-8B-C7-F7-90:CMCC  120.197.40.4    rank.ie.sogou.com   搜索引擎    28  27  3659    3538    200
1363157986029   15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99  www.umeng.com   站点统计    3   3   1938    180 200
1363157992093   13560439658 C4-17-FE-BA-DE-D9:CMCC  120.196.100.99          15  9   918 4938    200
1363157986041   13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4            3   3   180 180 200
1363157984040   13602846565 5C-0E-8B-8B-B6-00:CMCC  120.197.40.4    2052.flash2-http.qq.com 综合门户    15  12  1938    2910    200
1363157995093   13922314466 000-FD-07-A2-EC-BA:CMCC  120.196.100.82  img.qfc.cn      12  12  3008    3720    200
1363157982040   13502468823 5C-000A0-050B0-060A0-000B-D4:CMCC-EASY 120.196.100.99  y0.ifengimg.com 综合门户    57  102 7335    110349  200
1363157986072   18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99  input.shouji.sogou.com  搜索引擎    21  18  9531    2412    200
1363157990043   13925057413 00-1F-64-E1-E6-9A:CMCC  120.196.100.55  t3.baidu.com    搜索引擎    69  63  11058   48243   200
1363157988072   13760778710 00-FD-07-A4-7B-08:CMCC  120.196.100.82          2   2   120 120 200
1363157985066   13726238888 00-FD-07-A4-72-B8:CMCC  120.196.100.82  i02.c.aliimg.com        24  27  2481    24681   200
1363157993055   13560436666 C4-17-FE-BA-DE-D9:CMCC  120.196.100.99          18  15  1116    954 200

Partitioner

package com.neusoft;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;


import java.util.HashMap;

public class FlowPartitioner extends Partitioner<Text, FlowBean> {
   private static HashMap<String, Integer> typeMap = new HashMap<String, Integer>();
   static {
       typeMap.put("135",0);
       typeMap.put("136",1);
       typeMap.put("137",2);
       typeMap.put("138",3);
   }

   @Override
   public int getPartition(Text text, FlowBean flowBean, int i) {
       String sub = text.toString().substring(0, 3);
       Integer o = (Integer) typeMap.get(sub);
       return o==null?4:o;
   }
}

Driver部分代码：
追加

job.setNumReduceTasks(5);
        job.setPartitionerClass(FlowPartitioner.class);