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目录
Order by：
order by : 是全局排序，只会启动给一个Reducer来完成全排序。即便我们一开始在跑job的时候，就设置Reducer的个数为多个
Hive最终还是交给一个Reducer来处理。Reducer的个数默认是-1，即Hive按照自己的规则来设置Reducer的个数。
查询员工信息按工资升序排列
select * from emp order by sal;
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| emp.empno | emp.ename | emp.job | emp.mgr | emp.hiredata | emp.sal | emp.comm | emp.deptno |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| 7369 | SMITH | CLERK | 7902 | 1980-12-17 | 800.0 | NULL | 20 |
| 7900 | JAMES | CLERK | 7698 | 1981-12-3 | 950.0 | NULL | 30 |
| 7876 | ADAMS | CLERK | 7788 | 1987-5-23 | 1100.0 | NULL | 20 |
| 7521 | WARD | SALESMAN | 7698 | 1981-2-22 | 1250.0 | 500.0 | 30 |
| 7654 | MARTIN | SALESMAN | 7698 | 1981-9-28 | 1250.0 | 1400.0 | 30 |
| 7934 | MILLER | CLERK | 7782 | 1982-1-23 | 1300.0 | NULL | 10 |
| 7844 | TURNER | SALESMAN | 7698 | 1981-9-8 | 1500.0 | 0.0 | 30 |
| 7499 | ALLEN | SALESMAN | 7698 | 1981-2-20 | 1600.0 | 300.0 | 30 |
| 7782 | CLARK | MANAGER | 7839 | 1981-6-9 | 2450.0 | NULL | 10 |
| 7698 | BLAKE | MANAGER | 7839 | 1981-5-1 | 2850.0 | NULL | 30 |
| 7566 | JONES | MANAGER | 7839 | 1981-4-2 | 2975.0 | NULL | 20 |
| 7788 | SCOTT | ANALYST | 7566 | 1987-4-19 | 3000.0 | NULL | 20 |
| 7902 | FORD | ANALYST | 7566 | 1981-12-3 | 3000.0 | NULL | 20 |
| 7839 | KING | PRESIDENT | NULL | 1981-11-17 | 5000.0 | NULL | 10 |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
查询员工信息按工资降序排列
select * from emp order by sal desc;
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| emp.empno | emp.ename | emp.job | emp.mgr | emp.hiredata | emp.sal | emp.comm | emp.deptno |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| 7839 | KING | PRESIDENT | NULL | 1981-11-17 | 5000.0 | NULL | 10 |
| 7902 | FORD | ANALYST | 7566 | 1981-12-3 | 3000.0 | NULL | 20 |
| 7788 | SCOTT | ANALYST | 7566 | 1987-4-19 | 3000.0 | NULL | 20 |
| 7566 | JONES | MANAGER | 7839 | 1981-4-2 | 2975.0 | NULL | 20 |
| 7698 | BLAKE | MANAGER | 7839 | 1981-5-1 | 2850.0 | NULL | 30 |
| 7782 | CLARK | MANAGER | 7839 | 1981-6-9 | 2450.0 | NULL | 10 |
| 7499 | ALLEN | SALESMAN | 7698 | 1981-2-20 | 1600.0 | 300.0 | 30 |
| 7844 | TURNER | SALESMAN | 7698 | 1981-9-8 | 1500.0 | 0.0 | 30 |
| 7934 | MILLER | CLERK | 7782 | 1982-1-23 | 1300.0 | NULL | 10 |
| 7654 | MARTIN | SALESMAN | 7698 | 1981-9-28 | 1250.0 | 1400.0 | 30 |
| 7521 | WARD | SALESMAN | 7698 | 1981-2-22 | 1250.0 | 500.0 | 30 |
| 7876 | ADAMS | CLERK | 7788 | 1987-5-23 | 1100.0 | NULL | 20 |
| 7900 | JAMES | CLERK | 7698 | 1981-12-3 | 950.0 | NULL | 30 |
| 7369 | SMITH | CLERK | 7902 | 1980-12-17 | 800.0 | NULL | 20 |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
按照部门和工资升序排序
select * from emp order by deptno,sal;
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| emp.empno | emp.ename | emp.job | emp.mgr | emp.hiredata | emp.sal | emp.comm | emp.deptno |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| 7934 | MILLER | CLERK | 7782 | 1982-1-23 | 1300.0 | NULL | 10 |
| 7782 | CLARK | MANAGER | 7839 | 1981-6-9 | 2450.0 | NULL | 10 |
| 7839 | KING | PRESIDENT | NULL | 1981-11-17 | 5000.0 | NULL | 10 |
| 7369 | SMITH | CLERK | 7902 | 1980-12-17 | 800.0 | NULL | 20 |
| 7876 | ADAMS | CLERK | 7788 | 1987-5-23 | 1100.0 | NULL | 20 |
| 7566 | JONES | MANAGER | 7839 | 1981-4-2 | 2975.0 | NULL | 20 |
| 7788 | SCOTT | ANALYST | 7566 | 1987-4-19 | 3000.0 | NULL | 20 |
| 7902 | FORD | ANALYST | 7566 | 1981-12-3 | 3000.0 | NULL | 20 |
| 7900 | JAMES | CLERK | 7698 | 1981-12-3 | 950.0 | NULL | 30 |
| 7654 | MARTIN | SALESMAN | 7698 | 1981-9-28 | 1250.0 | 1400.0 | 30 |
| 7521 | WARD | SALESMAN | 7698 | 1981-2-22 | 1250.0 | 500.0 | 30 |
| 7844 | TURNER | SALESMAN | 7698 | 1981-9-8 | 1500.0 | 0.0 | 30 |
| 7499 | ALLEN | SALESMAN | 7698 | 1981-2-20 | 1600.0 | 300.0 | 30 |
| 7698 | BLAKE | MANAGER | 7839 | 1981-5-1 | 2850.0 | NULL | 30 |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
由于order by只会启动一个reduce来进行排序，假如我们的数据量非常的大，内存是放不下的，那将会怎么办呢？我们使用sort by
来实现区内（分区）排序Sort by：
Sort By：对于大规模的数据集order by的效率非常低。在很多情况下，并不需要全局排序，此时可以使用sort by。Sort by为每个reducer产生一个排序文件。每个Reducer内部进行排序，对全局结果集来说不是排序。
1，设置reduce的个数为3，并查询reduce的个数：
0: jdbc:hive2://hadoop108:10000> set mapreduce.job.reduces=3;
No rows affected (0.016 seconds)
0: jdbc:hive2://hadoop108:10000> set mapreduce.job.reduces;
+--------------------------+--+
| set |
+--------------------------+--+
| mapreduce.job.reduces=3 |
+--------------------------+--+
1 row selected (0.009 seconds)
2，根据部门编号升序查看员工信息
select * from emp sort by deptno;
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| emp.empno | emp.ename | emp.job | emp.mgr | emp.hiredata | emp.sal | emp.comm | emp.deptno |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
| 7782 | CLARK | MANAGER | 7839 | 1981-6-9 | 2450.0 | NULL | 10 |
| 7839 | KING | PRESIDENT | NULL | 1981-11-17 | 5000.0 | NULL | 10 |
| 7788 | SCOTT | ANALYST | 7566 | 1987-4-19 | 3000.0 | NULL | 20 |
| 7654 | MARTIN | SALESMAN | 7698 | 1981-9-28 | 1250.0 | 1400.0 | 30 |
| 7698 | BLAKE | MANAGER | 7839 | 1981-5-1 | 2850.0 | NULL | 30 |
| 7844 | TURNER | SALESMAN | 7698 | 1981-9-8 | 1500.0 | 0.0 | 30 |
| 7934 | MILLER | CLERK | 7782 | 1982-1-23 | 1300.0 | NULL | 10 |
| 7876 | ADAMS | CLERK | 7788 | 1987-5-23 | 1100.0 | NULL | 20 |
| 7566 | JONES | MANAGER | 7839 | 1981-4-2 | 2975.0 | NULL | 20 |
| 7900 | JAMES | CLERK | 7698 | 1981-12-3 | 950.0 | NULL | 30 |
| 7521 | WARD | SALESMAN | 7698 | 1981-2-22 | 1250.0 | 500.0 | 30 |
| 7499 | ALLEN | SALESMAN | 7698 | 1981-2-20 | 1600.0 | 300.0 | 30 |
| 7902 | FORD | ANALYST | 7566 | 1981-12-3 | 3000.0 | NULL | 20 |
| 7369 | SMITH | CLERK | 7902 | 1980-12-17 | 800.0 | NULL | 20 |
+------------+------------+------------+----------+---------------+----------+-----------+-------------+--+
这里使用了三个Reducer进行排序，可以观察到deptno是有三个阶段的。
3，根据部门编号降序序查看员工信息，并输出到本地文件中：
insert overwrite local directory '/opt/module/data/export'
row format delimited
fields terminated by '\t'
select * from emp sort by deptno desc;
[isea@hadoop108 export]$ ll
总用量 12
-rw-r--r--. 1 isea isea 288 12月 2 01:48 000000_0
-rw-r--r--. 1 isea isea 282 12月 2 01:48 000001_0
-rw-r--r--. 1 isea isea 91 12月 2 01:48 000002_0
[isea@hadoop108 export]$ cat 000000_0
7844 TURNER SALESMAN 7698 1981-9-8 1500.0 0.0 30
7698 BLAKE MANAGER 7839 1981-5-1 2850.0 \N 30
7654 MARTIN SALESMAN 7698 1981-9-28 1250.0 1400.0 30
7788 SCOTT ANALYST 7566 1987-4-19 3000.0 \N 20
7839 KING PRESIDENT \N 1981-11-17 5000.0 \N 10
7782 CLARK MANAGER 7839 1981-6-9 2450.0 \N 10
[isea@hadoop108 export]$ pwd
/opt/module/data/export
那么Hive的分区规则是什么呢？这里使用的是随机分的，目的是为了避免数据倾斜。
可见，sort by 无法实现定制化的分区，假如我们想让某个字段一样的进入同一个分区，我们就需要使用distributed by
Distribute by：
在有些情况下，我们需要控制某个特定行应该到哪个reducer，通常是为了进行后续的聚集操作。distribute by 子句可以做这件事。distribute by类似MR中partition（自定义分区），进行分区，结合sort by使用。
1,先按照部门编号分区，再按照员工编号降序排序。
insert overwrite local directory '/opt/module/data/export/distributed_by'
row format delimited
fields terminated by '\t'
select empno,ename,deptno from emp distribute by deptno sort by empno desc;
[isea@hadoop108 distributed_by]$ ll
总用量 12
-rw-r--r--. 1 isea isea 85 12月 2 02:05 000000_0
-rw-r--r--. 1 isea isea 42 12月 2 02:05 000001_0
-rw-r--r--. 1 isea isea 69 12月 2 02:05 000002_0
[isea@hadoop108 distributed_by]$ cat 000000_0
7900 JAMES 30
7844 TURNER 30
7698 BLAKE 30
7654 MARTIN 30
7521 WARD 30
7499 ALLEN 30
[isea@hadoop108 distributed_by]$ cat 000001
cat: 000001: 没有那个文件或目录
[isea@hadoop108 distributed_by]$ cat 000001_0
7934 MILLER 10
7839 KING 10
7782 CLARK 10
[isea@hadoop108 distributed_by]$ pwd
/opt/module/data/export/distributed_by
distribute by的分区规则是根据分区字段的hash码与reduce的个数进行模除后，余数相同的分到一个区。
Hive要求DISTRIBUTE BY语句要写在SORT BY语句之前。先分区，后对区内的数据进行排序。
Cluster by：
当distribute by和sorts by字段相同时，可以使用cluster by方式。cluster by除了具有distribute by的功能外还兼具sort by的功能。但是排序只能是升序排序，不能指定排序规则为ASC或者DESC。
1,先按照部门分区，在按照部门排序：
set mapreduce.job.reduces=4;
由于使用的是Hadoop默认的分区规则，这里的部门编号是 10,20,30 对 3取余之后分布在 1,2,0 区，看不出来效果，
因此将reduce的个数设置为4，分区的情况如下：
0分区：20
1分区：没有
2分区：30,10，这里10和30分到同样的一个区，并按照deptno升序排列
3分区：没有
insert overwrite local directory '/opt/module/data/export/cluster_by'
row format delimited
fields terminated by '\t'
select ename, deptno from emp cluster by deptno;
[isea@hadoop108 cluster_by]$ cat 000000_0
FORD 20
SCOTT 20
JONES 20
ADAMS 20
SMITH 20
[isea@hadoop108 cluster_by]$ cat 000002_0
MILLER 10
KING 10
CLARK 10
BLAKE 30
MARTIN 30
JAMES 30
WARD 30
ALLEN 30
TURNER 30
[isea@hadoop108 cluster_by]$ pwd
/opt/module/data/export/cluster_by