结果说明：sort by 可以通过mapred.reduce.task指定reduce个数，可以将查询后的数据分别写入两个不同的reduce文件中。
---set mapred.reduce.tasks=2，使用order by
hive> set mapred.reduce.tasks=2;
hive> insert overwrite local directory '/tmp/hivetest/orderby' select id,devid,job_time from tb_in_base where job_time=030729 order by devid;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks determined at compile time: 1 //注：说明只有一个reducer
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_201307151509_15469, Tracking URL = http://mwtec-50:50030/jobdetails.jsp jobid=job_201307151509_15469
Kill Command = /home/hadoop/hadoop-0.20.2/bin/hadoop job -Dmapred.job.tracker=mwtec-50:9002 -kill job_201307151509_15469
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2013-08-05 18:14:22,528 Stage-1 map = 0%, reduce = 0%
2013-08-05 18:14:24,538 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:25,548 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:26,562 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:27,568 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:28,574 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:29,581 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:30,587 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.05 sec
2013-08-05 18:14:31,592 Stage-1 map = 100%, reduce = 33%, Cumulative CPU 1.05 sec
2013-08-05 18:14:32,598 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.56 sec
2013-08-05 18:14:33,604 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.56 sec
2013-08-05 18:14:34,611 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 2.56 sec
MapReduce Total cumulative CPU time: 2 seconds 560 msec
Ended Job = job_201307151509_15469
Copying data to local directory /tmp/hivetest/orderby
Copying data to local directory /tmp/hivetest/orderby
7 Rows loaded to /tmp/hivetest/orderby
MapReduce Jobs Launched:
Job 0: Map: 1 Reduce: 1 Cumulative CPU: 2.56 sec HDFS Read: 458 HDFS Write: 112 SUCCESS
Total MapReduce CPU Time Spent: 2 seconds 560 msec
OK
Time taken: 16.804 seconds
查看/tmp/hivetest/orderby下查询结果
结果说明：设置mapred.reduce.task=2，使用order by做排序，所有的数据都是写入同一个文件中。由此可说明不论设计多少个reduce任务数，order by 只使用一个reduce。
注：可以用limit 子句大大减少数据量，使用limit n ，后，传输到reduce商的数据记录就减少到n* map，否则可能由于数据过大可能出不了结果。
疑问：使用limit n之后，数据是减少了，但是对于统计数据的话，少了数据的统计还有意义吗？
2.3 distribute by
distribute by 是控制在map端如何拆分给reduce端。根据distribute by 后面的列及reduce个数进行数据分发，默认采用hash算法。distribute可以使用length方法会根据string类型的长度划分到不同的reduce中，最终输出到不同的文件中。 length 是内建函数，也可以指定其他的函数或这使用自定义函数。
注：对distribute by 进行测试，一定要分配多个reduce进行处理，否则无法看到distribute by 效果。
--使用id进行排序且set mapred.reduce.tasks=2;
hive> set mapred.reduce.tasks=2;
hive> insert overwrite local directory '/tmp/hivetest/distributeby' select id,devid,job_time from tb_in_base where job_time=030729 and id<8 distribute by id ;
Total MapReduce jobs = 1
Launching Job 1 out of 1