hive的distribute by应用

hive中的distribute by是控制在map端如何拆分数据给reduce端的。
hive会根据distribute by后面列，根据reduce的个数进行数据分发，默认是采用hash算法。

对于distribute by进行测试，一定要分配多reduce进行处理，否则无法看到distribute by的效果。

hive> select * from test09;
OK
100 tom
200 mary
300 kate
400 tim
Time taken: 0.061 seconds

hive> insert overwrite local directory ‘/home/hjl/sunwg/ooo’ select * from test09 distribute by id;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Defaulting to jobconf value of: 2
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_201105020924_0070, Tracking URL = http://hadoop00:50030/jobdetails.jsp?jobid=job_201105020924_0070
Kill Command = /home/hjl/hadoop/bin/../bin/hadoop job -Dmapred.job.tracker=hadoop00:9001 -kill job_201105020924_0070
2011-05-03 06:12:36,644 Stage-1 map = 0%, reduce = 0%
2011-05-03 06:12:37,656 Stage-1 map = 50%, reduce = 0%
2011-05-03 06:12:39,673 Stage-1 map = 100%, reduce = 0%
2011-05-03 06:12:44,713 Stage-1 map = 100%, reduce = 50%
2011-05-03 06:12:46,733 Stage-1 map = 100%, reduce = 100%
Ended Job = job_201105020924_0070
Copying data to local directory /home/hjl/sunwg/ooo
Copying data to local directory /home/hjl/sunwg/ooo
4 Rows loaded to /home/hjl/sunwg/ooo
OK
Time taken: 17.663 seconds

第一次执行根据id字段来做分发，结果如下：

[hjl@sunwg src]$ cat /home/hjl/sunwg/ooo/attempt_201105020924_0070_r_000000_0
400tim
200mary
[hjl@sunwg src]$ cat /home/hjl/sunwg/ooo/attempt_201105020924_0070_r_000001_0
300kate
100tom

这次我们换个分发的方式，采用length(id)的结果，因为这几条记录的id字段的长度都相同，所以应该会被分布到同一个reduce中。

hive> insert overwrite local directory ‘/home/hjl/sunwg/lll’ select * from test09 distribute by length(id);
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Defaulting to jobconf value of: 2
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=
In order to set a constant number of reducers:
set mapred.reduce.tasks=
Starting Job = job_201105020924_0071, Tracking URL = http://hadoop00:50030/jobdetails.jsp?jobid=job_201105020924_0071
Kill Command = /home/hjl/hadoop/bin/../bin/hadoop job -Dmapred.job.tracker=hadoop00:9001 -kill job_201105020924_0071
2011-05-03 06:15:21,430 Stage-1 map = 0%, reduce = 0%
2011-05-03 06:15:24,454 Stage-1 map = 100%, reduce = 0%
2011-05-03 06:15:31,509 Stage-1 map = 100%, reduce = 50%
2011-05-03 06:15:34,539 Stage-1 map = 100%, reduce = 100%
Ended Job = job_201105020924_0071
Copying data to local directory /home/hjl/sunwg/lll
Copying data to local directory /home/hjl/sunwg/lll
4 Rows loaded to /home/hjl/sunwg/lll
OK
Time taken: 20.632 seconds

在查看下结果是否和我们的预期相同：
[hjl@sunwg src]$ cat /home/hjl/sunwg/lll/attempt_201105020924_0071_r_000000_0
[hjl@sunwg src]$ cat /home/hjl/sunwg/lll/attempt_201105020924_0071_r_000001_0
100tom
200mary
300kate
400tim

文件attempt_201105020924_0071_r_000000_0中没有记录，而全部的记录都在attempt_201105020924_0071_r_000001_0中。