4.HiveSQL查询语句方法大全
2024-07-04 08:54:03
4. 查询语句
4.1 语法规则与数据准备
官方网址
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Select
基本查询语句语法：
SELECT [ALL | DISTINCT] select_expr, select_expr, ...FROM table_reference[WHERE where_condition][GROUP BY col_list][ORDER BY col_list][CLUSTER BY col_list| [DISTRIBUTE BY col_list] [SORT BY col_list]][LIMIT [offset,] rows]select [all | distinct] select_expr, select_expr, ...from table_reference[where where_condition][group by col_list][order by col_list][cluster by col_list| [distribute by col_list] [sort by col_list]][limit [offset,] rows]数据准备：
原始数据
部门表：dept
10 accounting 1700
20 research 1800
30 sales 1900
40 operations 1700雇员表emp：
7369 smith clerk 7902 1980-12-17 800.00 20
7499 allen salesman 7698 1981-2-20 1600.00 300.00 30
7521 ward salesman 7698 1981-2-22 1250.00 500.00 30
7566 jones manager 7839 1981-4-2 2975.00 20
7654 martin salesman 7698 1981-9-28 1250.00 1400.00 30
7698 blake manager 7839 1981-5-1 2850.00 30
7782 clark manager 7839 1981-6-9 2450.00 10
7788 scott analyst 7566 1987-4-19 3000.00 20
7839 king president 1981-11-17 5000.00 10
7844 turner salesman 7698 1981-9-8 1500.00 0.00 30
7876 adams clerk 7788 1987-5-23 1100.00 20
7900 james clerk 7698 1981-12-3 950.00 30
7902 ford analyst 7566 1981-12-3 3000.00 20
7934 miller clerk 7782 1982-1-23 1300.00 10插入数据
创建部门表
create table if not exists dept(
deptno int,
dname string,
loc int
)
row format delimited fields terminated by '\t';创建员工表
create table if not exists emp(
empno int,
ename string,
job string,
mgr int,
hiredate string,
sal double,
comm double,
deptno int)
row format delimited fields terminated by '\t';在node4将数据写到/root/data/下面的两个文件中
[root@node4 data]# vim dept.txt
[root@node4 data]# vim emp.txt
[root@node4 data]# cat dept.txt
10 ccounting 1700
20 research 1800
30 sales 1900
40 operations 1700[root@node4 data]# cat emp.txt
7369 mith clerk 7902 1980-12-17 800.00 20
7499 allen salesman 7698 1981-2-20 1600.00 300.00 30
7521 ward salesman 7698 1981-2-22 1250.00 500.00 30
7566 jones manager 7839 1981-4-2 2975.00 20
7654 martin salesman 7698 1981-9-28 1250.00 1400.00 30
7698 blake manager 7839 1981-5-1 2850.00 30
7782 clark manager 7839 1981-6-9 2450.00 10
7788 scott analyst 7566 1987-4-19 3000.00 20
7839 king president 1981-11-17 5000.00 10
7844 turner salesman 7698 1981-9-8 1500.00 0.00 30
7876 adams clerk 7788 1987-5-23 1100.00 20
7900 james clerk 7698 1981-12-3 950.00 30
7902 ford analyst 7566 1981-12-3 3000.00 20
7934 miller clerk 7782 1982-1-23 1300.00 10[root@node4 data]# pwd
/root/datahive上操作：创建表+导
hive中创建表结构，hive中导入txt进入表
hive> create table if not exists dept(> deptno int,> dname string,> loc int> )> row format delimited fields terminated by '\t';
OK
Time taken: 0.829 seconds
hive> load data local inpath '/root/data/dept.txt' into table dept;
Loading data to table default.dept
OK
Time taken: 0.916 seconds
hive> create table if not exists emp(> empno int,> ename string,> job string,> mgr int,> hiredate string, > sal double, > comm double,> deptno int)> row format delimited fields terminated by '\t';
OK
Time taken: 0.226 seconds
hive> load data local inpath '/root/data/emp.txt' into table emp;
Loading data to table default.emp
OK
Time taken: 0.599 seconds
4.2 基本查询
4.2.1 全表和指定列查询
全表查询：使用*表示所有的列
hive> select * from dept;
OK
10 ccounting 1700
20 research 1800
30 sales 1900
40 operations 1700
Time taken: 0.424 seconds, Fetched: 4 row(s)hive> select * from emp;
OK
7369 smith clerk 7902 1980-12-17 800.0 NULL 20
7499 allen salesman 7698 NULL 1600.00 300.0 30.0 NULL
7521 ward salesman 7698 NULL 1250.00 500.00 30.0 NULL NULL
Time taken: 0.401 seconds, Fetched: 15 row(s)hive中查询指定列：select后加上具体列名称
hive> select deptno,dname from dept;
OK
10 ccounting
20 research
30 sales
40 operations
Time taken: 0.442 seconds, Fetched: 4 row(s)SQL语言对大小写不敏感
可以写在一行or多行都行
关键词不能被缩写，不能被分行
复杂sql一般得分行来写
使用缩进符提高语句可读性
4.2.2 列别名
--查询雇员的名称和部门
hive> select ename name, deptno as no from emp;
OK
smith 20
allen salesman NULL
ward salesman NULL
jones manager 7839 NULL
martin salesman NULL
blake manager 7839 NULL
clark manager 7839 NULL
scott analyst 7566 NULL
king president NULL
turner salesman NULL
adams clerk 7788 1987-5-23 NULL
james clerk 7698 1981-12-3 NULL
ford analyst 7566 1981-12-3 NULL
miller clerk 7782 1982-1-23 NULL
NULL NULL
Time taken: 0.443 seconds, Fetched: 15 row(s)
......
hive列别名如何使用：在列名后面直接跟别名或使用as关键字跟列别名
使用列别名的好处：简化使用。
4.2.3 算术运算符
演示
hive> select ename,sal*12 from emp;
OK
smith 9600.0
allen 19200.0
ward 15000.0
jones 35700.0
martin 15000.0
blake 34200.0
clark 29400.0
scott 36000.0
king 60000.0
turner 18000.0
adams 13200.0
james 11400.0
ford 36000.0
miller 15600.0
Time taken: 0.993 seconds, Fetched: 14 row(s)
hive> select ename,sal*12
year_money,sal*12+2000 year_all_money from emp;
OK
smith 9600.0 11600.0
allen 19200.0 21200.0
ward 15000.0 17000.0
jones 35700.0 37700.0
martin 15000.0 17000.0
blake 34200.0 36200.0
clark 29400.0 31400.0
scott 36000.0 38000.0
king 60000.0 62000.0
turner 18000.0 20000.0
adams 13200.0 15200.0
james 11400.0 13400.0
ford 36000.0 38000.0
miller 15600.0 17600.0
Time taken: 0.322 seconds, Fetched: 14 row(s)
4.2.4 常用的聚合函数
# 查询总共多少行
hive> select count(*) from emp;
hive> select count(empno) from emp;
# 查询最低工资
hive> select min(sal) from emp;
# 查询最高工资
hive> select max(sal) from emp;
# 查询平均工资
hive> select avg(sal) from emp;
# 查询和
hive> select sum(sal) from emp;
4.2.5 where语句
使用where语句，作用：将不符合条件的数据过滤掉。
#查询工资大于2500的所有雇员。
hive> select * from emp where sal>2500;
OK
7566 jones manager 7839 1981-4-2
2975.0 NULL 20
7698 blake manager 7839 1981-5-1
2850.0 NULL 30
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7902 ford analyst 7566 1981-12-3
3000.0 NULL 20
hive> select * from emp where sal>2500 and
deptno=20;
OK
128
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
Time taken: 0.504 seconds, Fetched: 3 row(s)
hive> select * from emp where sal>2500 and
deptno!=20;
OK
7698 blake manager 7839 1981-5-1
2850.0 NULL 30
7839 king president NULL 1981-11-17
5000.0 NULL 10
Time taken: 0.391 seconds, Fetched: 2 row(s)
4.2.6 limit语句
使用limit实现查询指定的行数。
hive> select * from emp limit 8;
OK
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7566 jones manager 7839 1981-4-2
2975.0 NULL 20
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
129
130
7698 blake manager 7839 1981-5-1
2850.0 NULL 30
7782 clark manager 7839 1981-6-9
2450.0 NULL 10
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
Time taken: 0.434 seconds, Fetched: 8 row(s)
hive> select * from emp limit 0,5;
OK
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7566 jones manager 7839 1981-4-2
2975.0 NULL 20
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
Time taken: 0.308 seconds, Fetched: 5 row(s)
hive> select * from emp limit 5,5;
OK
7698 blake manager 7839 1981-5-1
2850.0 NULL 30
7782 clark manager 7839 1981-6-9
2450.0 NULL 10
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
Time taken: 0.293 seconds, Fetched: 5 row(s)
# limit语句和where语句可以一起使用，一起使用时，需要放在where语句的后面
hive> select * from emp where sal>1000 limit 5;
OK
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7566 jones manager 7839 1981-4-2
2975.0 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
Time taken: 0.307 seconds, Fetched: 5 row(s)
4.2.7 比较运算符
通常用在where语句、having语句、join … on
# 查询工资等于1600的
hive> select * from emp where sal = 1600;
OK
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
Time taken: 0.313 seconds, Fetched: 1 row(s)
# 查询工资 [1000,2000]所有雇员
hive> select * from emp where sal between 1000
and 2000;
OK
133
134
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
Time taken: 0.31 seconds, Fetched: 6 row(s)
# 查询工资不在 [1000,2000]区间的所有雇员 <1000 和
>2000的
hive> select * from emp where sal not between
1000 and 2000;
OK
7369 smith clerk 7902 1980-12-17
800.0 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
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7900 james clerk 7698 1981-12-3
950.0 NULL 30
7902 ford analyst 7566 1981-12-3
3000.0 NULL 20
Time taken: 0.305 seconds, Fetched: 8 row(s)
#查询 comm列的值为null的
hive> select * from emp where comm is null;
OK
7369 smith clerk 7902 1980-12-17
800.0 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
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7900 james clerk 7698 1981-12-3
950.0 NULL 30
7902 ford analyst 7566 1981-12-3
3000.0 NULL 20
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
Time taken: 0.335 seconds, Fetched: 10 row(s)
#查询 comm列的值不为null的
hive> select * from emp where comm is not null;
OK
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
136
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
# 查询工资1500或1600的所有雇员
hive> select * from emp where sal in
(1500,1600);
OK
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
like和rlike：模糊查询，查询指定列中包含关键字的。
通配符：
% 代表0个或多个任意字符
_ 代表1个任意字符
rlike:是hive中的一个扩展功能，可以通过正则表达式指定匹配的条件。
案例实战：
#查询名称以字符m开头的所有雇员
hive> select * from emp where ename like 'm%';
OK
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
137
Time taken: 0.282 seconds, Fetched: 2 row(s)
#查询名称中第二字符是m的所有雇员的信息
hive> select * from emp where ename like '_m%';
OK
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
Time taken: 0.335 seconds, Fetched: 1 row(s)
# like查询名称中包含m的所有雇员的信息
hive> select * from emp where ename like '%m%';
OK
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7900 james clerk 7698 1981-12-3
950.0 NULL 30
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
Time taken: 0.293 seconds, Fetched: 5 row(s)
# rlike查询名称中包含m的所有雇员的信息
hive> select * from emp where ename rlike
'[m]';
OK
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7900 james clerk 7698 1981-12-3
950.0 NULL 30
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
Time taken: 0.262 seconds, Fetched: 5 row(s)
4.2.8 逻辑运算符
案例实战：
查询薪资大于1000，并且奖金大于等于500
hive> select * from emp where sal>1000 and comm >=500;
OK
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
查询薪资大于1000，或者奖金大于等于500
hive> select * from emp where sal>1000 or comm>=500;
OK
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7566 jones manager 7839 1981-4-2
2975.0 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
7782 clark manager 7839 1981-6-9
2450.0 NULL 10
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7902 ford analyst 7566 1981-12-3
3000.0 NULL 20
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
查询除了10和30部门下的所有雇员
4.3 分组
4.3.1 group by语句
group by通常和聚合函数一起使用，按照一个或者多个列进行分组，然后在对每组数据进行聚合操作。
案例实战：
查询每个部门的平均工资、最高工资、最低工资
hive> select deptno,avg(sal) avg_sal,max(sal)
max_sal,min(sal) min_sal from emp group by
deptno;
Query ID = root_20211115150801_985c1ac7-9260-
423b-a9b0-72eae86b3b92
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated
from input data size: 1
In order to change the average load for a
reducer (in bytes):set hive.exec.reducers.bytes.per.reducer=
<number>
In order to limit the maximum number of
reducers:set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:set mapreduce.job.reduces=<number>
Starting Job = job_1636950452604_0003, Tracking
URL =
http://node4:8088/proxy/application_16369504526
04_0003/
Kill Command = /opt/hadoop-3.1.3/bin/mapred job
-kill job_1636950452604_0003
Hadoop job information for Stage-1: number of
mappers: 1; number of reducers: 1
10 2916.6666666666665 5000.0 1300.0
20 2175.0 3000.0 800.0
30 1566.6666666666667 2850.0 950.0
使用group by语句后，select语句中执行出现group by语句中的列名和聚合函数
hive> select deptno,avg(sal) avg_sal,max(sal) max_sal,min(sal) min_sal,ename from emp group by deptno;
FAILED: SemanticException [Error 10025]: Line
1:65 Expression not in GROUP BY key 'ename'
4.3.2 having语句
where语句中不能出现聚合函数，或者通过聚合函数计算的结果。
having语句只能用在group by语句的后面，在一个没有group by语句的sql中不能出现having语句。
案例实战：
查询每个部门平均薪资大于1800的部门的平均工资、最高工资、最低工资。
hive> select deptno,avg(sal) avg_sal,max(sal)
max_sal,min(sal) min_sal from emp group by
deptno where avg_sal>1800;
FAILED: ParseException line 1:90 missing EOF at
'where' near 'deptno'
hive> select deptno,avg(sal) avg_sal,max(sal)
max_sal,min(sal) min_sal from emp group by
deptno having avg_sal>1800;
Query ID = root_20211115151258_0b1728f2-27f6-
4b33-84aa-15a93421212b
Total jobs = 1
Launching Job 1 out of 1
4.4 关联查询
4.4.1 表别名
好处：简化查询和提交查询效率
案例实战：
hive> select e.empno,e.ename,d.deptno,d.dname> from emp e,dept as d> where e.deptno = d.deptno;
Query ID = root_20211115153044_53c8363f-fe06-
40ec-95ab-4d85e4c0d521
Total jobs = 1
Execution completed successfully
7369 smith 20 research
7499 allen 30 sales
7521 ward 30 sales
7566 jones 20 research
7654 martin 30 sales
7698 blake 30 sales
7782 clark 10 accounting
7788 scott 20 research
7839 king 10 accounting
7844 turner 30 sales
7876 adams 20 research
7900 james 30 sales
7902 ford 20 research
7934 miller 10 accounting
4.4.2 笛卡尔积
hive> select e.empno,e.ename,d.deptno,d.dname> from emp e,dept as d;
Warning: Map Join MAPJOIN[9][bigTable=?] in
task 'Stage-3:MAPRED' is a cross product
Query ID = root_20211115153651_bd271024-3bd7-
4355-acad-2c74042c5938
Total jobs = 1
Execution completed successfully
7900 james 10 accounting
7900 james 20 research
7900 james 30 sales
7900 james 40 operations
7902 ford 10 accounting
7902 ford 20 research
7902 ford 30 sales
7902 ford 40 operations
7934 miller 10 accounting
7934 miller 20 research
7934 miller 30 sales
7934 miller 40 operations
Time taken: 43.827 seconds, Fetched: 56 row(s)
笛卡尔积：在省略了连接条件或连接条件无效是会出现笛卡尔积。
编写sql时要避免出现笛卡尔积。将第一个表的每一条数据和第二表中的数据依次连接，第一个表中m行数据，第二表中有n行数据，笛卡尔积的行数为m*n行。
4.4.3 join语句
- 内连接
只有进行连接的两张表的数据都存在连接条件相匹配时才会查询出来。
hive> select e.empno,e.ename,d.dname from
emp e join dept d on e.deptno=d.deptno;
Query ID = root_20211118102413_dda90f63-
c584-4d88-997c-318ddfd18b6b
Total jobs = 1
7369 smith research
7499 allen sales
7521 ward sales
7566 jones research
7654 martin sales
7698 blake sales
7782 clark accounting
7788 scott research
7839 king accounting
7844 turner sales
7876 adams research
7900 james sales
7902 ford research
7934 miller accounting
- 右外连接
右外连接：join关键字右边的表所有的记录都会返回。
hive>select e.empno,e.ename,d.dname from
emp e right join dept d on
e.deptno=d.deptno;
7782 clark accounting
7839 king accounting
7934 miller accounting
7369 smith research
7566 jones research
7788 scott research
7876 adams research
7902 ford research
7499 allen sales
7521 ward sales
7654 martin sales
7698 blake sales
7844 turner sales
7900 james sales
NULL NULL operations #使用null替代
- 左外连接
左外连接：join关键字左边的表所有的记录都会返回。
hive>select e.empno,e.ename,d.dname from
emp e left join dept d on
e.deptno=d.deptno;
- 全外连接
hive>select e.empno,e.ename,d.dname from
emp e full join dept d on
e.deptno=d.deptno;
第一部分：两张有对应关联关系数据。
第二部分：左表中在右边表找不到匹配的数据。
第三部分：右表中在左边表找不到匹配的数据
4.5 排序
4.5.1 order by
全局排序，hql转换后的mr左右只有一个reduce任务。当数据量比较大时order by就要慎用，很有可能导致reduce需要较长的时间才能完成，或者完不成。
格式： order by 字段名 [asc|desc]
默认是asc 升序，desc表示降序
位置： order by语句通常防止hql语句的最后。
hive> select * from emp order by sal;
Query ID = root_20211118103903_c519efbd-9615-
431b-9a56-730616a097ba
Total jobs = 1
......
Hadoop job information for Stage-1: number of
mappers: 1; number of reducers: 1
......
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
148
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
Time taken: 33.587 seconds, Fetched: 14 row(s)
# 可以使用列的别名进行排序
hive> select empno,ename,sal*12 year_sal from
emp order by year_sal;
7369 smith 9600.0
7900 james 11400.0
7876 adams 13200.0
7521 ward 15000.0
7654 martin 15000.0
......
# 多列排序
hive> select empno,ename,deptno,sal from emp
order by deptno,sal;
7934 miller 10 1300.0
7782 clark 10 2450.0
7839 king 10 5000.0
7369 smith 20 800.0
7876 adams 20 1100.0
7566 jones 20 2975.0
7788 scott 20 3000.0
7902 ford 20 3000.0
7900 james 30 950.0
7654 martin 30 1250.0
7521 ward 30 1250.0
7844 turner 30 1500.0
7499 allen 30 1600
7698 blake 30 2850.0
#先按照部门编号从小到大排序，部门相同时，在按照sal从小到大排序
hive> select empno,ename,deptno,sal from emp
order by sal,deptno;
7369 smith 20 800.0
7900 james 30 950.0
7876 adams 20 1100.0
7521 ward 30 1250.0
7654 martin 30 1250.0
7934 miller 10 1300.0
7844 turner 30 1500.0
7499 allen 30 1600.0
7782 clark 10 2450.0
7698 blake 30 2850.0
7566 jones 20 2975.0
7788 scott 20 3000.0
7902 ford 20 3000.0
7839 king 10 5000.0
#先按照sal从小到大排序，sal相同时，在按照deptno从小到大排序
4.5.2 sort by
**sort by作用：**在每一个reduce task任务内部排序，在大量数据集时使用order by存在着效率低下的问题，很多场景中并不需要全局排序。
每个reduce任务都会对应的结果文件part-r-xxxxxx,在每一个结果文件中都是有序的，全局是无序的。
通过set命令设置reduce任务的数量，有效期是直到下次修改该参数的值或hive连接关闭：
# set 参数=value; 设置参数的值
hive> set mapreduce.job.reduces=3;
# set 参数; 查看reduce产生的值
hive> set mapreduce.job.reduces;
mapreduce.job.reduces=3
hive> select * from emp sort by deptno desc;
Query ID = root_20211118105647_6964a47b-fc4b-4907-8f35-ea3c9465ed59
Total jobs = 1
Launching Job 1 out of 1
......
Hadoop job information for Stage-1: number of
mappers: 1; number of reducers: 3
7844 turner salesman 7698 1981-9-8
1500.0 0.0 30
7698 blake manager 7839 1981-5-1
2850.0 NULL 30
7654 martin salesman 7698 1981-9-28
1250.0 1400.0 30
7788 scott analyst 7566 1987-4-19
3000.0 NULL 20
7839 king president NULL 1981-11-17
5000.0 NULL 10
7782 clark manager 7839 1981-6-9
2450.0 NULL 10
7521 ward salesman 7698 1981-2-22
1250.0 500.0 30
7499 allen salesman 7698 1981-2-20
1600.0 300.0 30
7900 james clerk 7698 1981-12-3
950.0 NULL 30
7876 adams clerk 7788 1987-5-23
1100.0 NULL 20
7566 jones manager 7839 1981-4-2
2975.0 NULL 20
7934 miller clerk 7782 1982-1-23
1300.0 NULL 10
7902 ford analyst 7566 1981-12-3
3000.0 NULL 20
7369 smith clerk 7902 1980-12-17
800.0 NULL 20
结果不够直观，将之后的结果文件下载到本地。
hive>insert overwrite local directory
'/opt/sortbyresult'
select * from emp sort by deptno desc;
node4查看文件：
[root@node4 ~]# cd /opt/sortbyresult/
[root@node4 sortbyresult]# pwd
/opt/sortbyresult
[root@node4 sortbyresult]# ls
000000_0 000001_0 000002_0
[root@node4 sortbyresult]# ll
总用量 12
-rw-r--r-- 1 root root 288 11月 18 11:01
000000_0
-rw-r--r-- 1 root root 282 11月 18 11:01
000001_0
-rw-r--r-- 1 root root 91 11月 18 11:01
000002_0
[root@node4 sortbyresult]# cat -A 000000_0
7844^Aturner^Asalesman^A7698^A1981-9-
8^A1500.0^A0.0^A30$
7698^Ablake^Amanager^A7839^A1981-5-
1^A2850.0^A\N^A30$
7654^Amartin^Asalesman^A7698^A1981-9-
28^A1250.0^A1400.0^A30$
7788^Ascott^Aanalyst^A7566^A1987-4-
19^A3000.0^A\N^A20$
7839^Aking^Apresident^A\N^A1981-11-
17^A5000.0^A\N^A10$
7782^Aclark^Amanager^A7839^A1981-6-
9^A2450.0^A\N^A10$
[root@node4 sortbyresult]# cat -A 000001_0
7521^Award^Asalesman^A7698^A1981-2-
22^A1250.0^A500.0^A30$
7499^Aallen^Asalesman^A7698^A1981-2-
20^A1600.0^A300.0^A30$
7900^Ajames^Aclerk^A7698^A1981-12-
3^A950.0^A\N^A30$
7876^Aadams^Aclerk^A7788^A1987-5-
23^A1100.0^A\N^A20$
7566^Ajones^Amanager^A7839^A1981-4-
2^A2975.0^A\N^A20$
7934^Amiller^Aclerk^A7782^A1982-1-
23^A1300.0^A\N^A10$
[root@node4 sortbyresult]# cat -A 000002_0
7902^Aford^Aanalyst^A7566^A1981-12-3^A3000.0^A\N^A20$
7369^Asmith^Aclerk^A7902^A1980-12-17^A800.0^A\N^A20$
4.5.3 distribute by 分区
**distribute by：**对应MR作业的partition（自定义分区），通常结合sort by一起使用。在某些情况下需要控制特定的行应该到哪个reduce任务中，为了后续的聚合操作。分区有对应reduce任务，有几个分区就有几个reduce任务；否则就看不到distribute by的效果。
实战演示：
先按照部门编号分区，再按照雇员编号的降序排序
hive> set mapreduce.job.reduces=4;
hive> insert overwrite local directory
'/opt/distributebyresult'
select * from emp distribute by deptno sort by
empno desc;
Query ID = root_20211118111723_4afc790e-a7e8-
4d5c-ba7a-8d790a379ea5
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified.
Defaulting to jobconf value of: 4
......
Hadoop job information for Stage-1: number of
mappers: 1; number of reducers: 4
在node4中查看结果文件：
[root@node4 distributebyresult]# pwd
/opt/distributebyresult
[root@node4 distributebyresult]# ll
总用量 8
-rw-r--r-- 1 root root 229 11月 18 11:18
000000_0
-rw-r--r-- 1 root root 0 11月 18 11:18
000001_0
-rw-r--r-- 1 root root 432 11月 18 11:18
000002_0
-rw-r--r-- 1 root root 0 11月 18 11:18
000003_0
[root@node4 distributebyresult]# cat -A
000000_0
7902^Aford^Aanalyst^A7566^A1981-12-
3^A3000.0^A\N^A20$
7876^Aadams^Aclerk^A7788^A1987-5-
23^A1100.0^A\N^A20$
7788^Ascott^Aanalyst^A7566^A1987-4-
19^A3000.0^A\N^A20$
7566^Ajones^Amanager^A7839^A1981-4-
2^A2975.0^A\N^A20$
7369^Asmith^Aclerk^A7902^A1980-12-
17^A800.0^A\N^A20$
[root@node4 distributebyresult]# cat -A
000001_0
[root@node4 distributebyresult]# cat -A
000002_0
7934^Amiller^Aclerk^A7782^A1982-1-
23^A1300.0^A\N^A10$
7900^Ajames^Aclerk^A7698^A1981-12-
3^A950.0^A\N^A30$
7844^Aturner^Asalesman^A7698^A1981-9-
8^A1500.0^A0.0^A30$
7839^Aking^Apresident^A\N^A1981-11-
17^A5000.0^A\N^A10$
7782^Aclark^Amanager^A7839^A1981-6-
9^A2450.0^A\N^A10$
7698^Ablake^Amanager^A7839^A1981-5-
1^A2850.0^A\N^A30$
7654^Amartin^Asalesman^A7698^A1981-9-
28^A1250.0^A1400.0^A30$
7521^Award^Asalesman^A7698^A1981-2-
22^A1250.0^A500.0^A30$
7499^Aallen^Asalesman^A7698^A1981-2-
20^A1600.0^A300.0^A30$
[root@node4 distributebyresult]# cat -A
000003_0
distribute by分区规则是根据分区字段的hash值与分区数（reduce任务的总数）进行除模后，余数相同的分到一个分区中。
要求：distribute by语句写在sort by语句的前面。
4.5.4 cluster by
当distribute by和sort by后面的字段相同时，可以使用cluster by进行简化。功能是等价的；但是只能使用升序排序，不能指定排序规则为asc或者desc。
distribute by是分区；sort by是排序
hive>select * from emp distribute by deptno sort by deptno;
#可以简化为
hive>select * from emp cluster by deptno;
hive>insert overwrite local directory
'/opt/clusterbyresult'
select * from emp cluster by deptno;
按照部门的编号进行分区，在reduce任务内部再按照部门的编号进行升序排序。使用部门编号求hash值%分区的数量 取余数，结果相同的数据被分到一个分区中。
node4上查看结果：
[root@node4 distributebyresult]# cd
/opt/clusterbyresult/
[root@node4 clusterbyresult]# ll
总用量 8
-rw-r--r-- 1 root root 229 11月 18 11:28
000000_0
-rw-r--r-- 1 root root 0 11月 18 11:28
000001_0
-rw-r--r-- 1 root root 432 11月 18 11:28
000002_0
-rw-r--r-- 1 root root 0 11月 18 11:28
000003_0
[root@node4 clusterbyresult]# cat -A 000000_0
7902^Aford^Aanalyst^A7566^A1981-12-
3^A3000.0^A\N^A20$
7788^Ascott^Aanalyst^A7566^A1987-4-
19^A3000.0^A\N^A20$
7566^Ajones^Amanager^A7839^A1981-4-
2^A2975.0^A\N^A20$
7876^Aadams^Aclerk^A7788^A1987-5-
23^A1100.0^A\N^A20$
7369^Asmith^Aclerk^A7902^A1980-12-
17^A800.0^A\N^A20$
[root@node4 clusterbyresult]# cat -A 000002_0
7934^Amiller^Aclerk^A7782^A1982-1-
23^A1300.0^A\N^A10$
7839^Aking^Apresident^A\N^A1981-11-
17^A5000.0^A\N^A10$
7782^Aclark^Amanager^A7839^A1981-6-
9^A2450.0^A\N^A10$
7698^Ablake^Amanager^A7839^A1981-5-
1^A2850.0^A\N^A30$
7654^Amartin^Asalesman^A7698^A1981-9-
28^A1250.0^A1400.0^A30$
7900^Ajames^Aclerk^A7698^A1981-12-
3^A950.0^A\N^A30$
7521^Award^Asalesman^A7698^A1981-2-
22^A1250.0^A500.0^A30$
7499^Aallen^Asalesman^A7698^A1981-2-
20^A1600.0^A300.0^A30$
7844^Aturner^Asalesman^A7698^A1981-9-
8^A1500.0^A0.0^A30$
4.6 基站掉话率分析实战
需求：找出掉话率最高的前10基站
创建原始数据表：
create table jizhan(
record_time string,
imei int,
cell string,
ph_num int,
call_num int,
drop_num int,
duration int,
drop_rate double,
net_type string,
erl int)
row format delimited fields terminated by ',';
字段描述
record_time：通话时间
imei：基站编号
cell：手机编号
drop_num：掉话的秒数
duration：通话持续总秒数
创建结果表：
create table jizhan_result(
imei string,
drop_num int,
duration int,
drop_rate double
);
首先将软件/data/cdr_summ_imei_cell_info.csv上传到node4上/root/data目录下，将第一行的表头数据删除
然后load到hive的jizhan表中
hive> load data local inpath
'/root/data/cdr_summ_imei_cell_info.csv' into
table jizhan;
Loading data to table default.jizhan
OK
Time taken: 2.734 seconds
hive> select * from jizhan limit 10;
OK
2011-07-13 00:00:00+08 356966 29448-37062 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 352024 29448-51331 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 353736 29448-51331 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 353736 29448-51333 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 351545 29448-51333 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 353736 29448-51343 1
0 0 8 0.0 0
2011-07-13 00:00:00+08 359681 29448-51462 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 354707 29448-51462 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 356137 29448-51470 0
0 0 0 0.0 0
2011-07-13 00:00:00+08 352739 29448-51971 0
0 0 0 0.0 0
hive> select count(*) from jizhan;
976305
编写分析的sql语句
select imei,sum(drop_num) sdrop,sum(duration) sdura,sum(drop_num)/sum(duration) drop_rate
from jizhan
group by imei
order by drop_rate desc;
将分析的结果写入到jizhan_result表中：
from jizhan
insert into jizhan_result
select imei,sum(drop_num) sdrop,sum(duration) sdura,sum(drop_num)/sum(duration) drop_rate
group by imei
order by drop_rate desc;
查询结果表，获取前10条数据，也就掉话率最高的前10个基站：
hive> select * from jizhan_result limit 10;
OK
639876 1 734 0.0013623978201634877
356436 1 1028 9.727626459143969E-4
351760 1 1232 8.116883116883117E-4
368883 1 1448 6.906077348066298E-4
358849 1 1469 6.807351940095302E-4
358231 1 1613 6.199628022318661E-4
863738 2 3343 5.982650314089142E-4
865011 1 1864 5.36480686695279E-4
862242 1 1913 5.227391531625719E-4
350301 2 3998 5.002501250625312E-4
4.HiveSQL查询语句方法大全相关推荐
- MySql查询语句方法
mysql表格查询方法: 查询: 1.简单查询 select * from Info --查所有数据 select Code,Name from Info --查指定列的数据 select Code ...
- SQL查询语句大全（转）
转自https://www.cnblogs.com/chenglc/p/8421492.html 重点参考https://blog.csdn.net/g1418377085/article/detai ...
- (转)经典SQL查询语句大全
一.基础 1.说明:创建数据库 CREATE DATABASE database-name 2.说明:删除数据库 drop database dbname 3.说明:备份sql server --- ...
- 经典的数据库查询语句大全
(转)经典SQL查询语句大全 (2011-05-25 21:49:10)转载▼ 标签: 杂谈 一.基础 1.说明:创建数据库 CREATE DATABASE database-name 2.说明:删除 ...
- 网上搜集的MySQL查询语句大全集锦(经典珍藏)
原文地址为: 网上搜集的MySQL查询语句大全集锦(经典珍藏) SQL查询语句大全集锦 MYSQL查询语句大全集锦 1:使用SHOW语句找出在服务器上当前存在什么数据库: mysql> SHOW ...
- orcal SQL查询语句大全集锦
orcal SQL查询语句大全集锦 一. 简单查询 简单的Transact-SQL查询只包括选择列表.FROM子句和WHERE子句.它们分别说明所查询列.查询的 表或视图.以及搜索条件等. 例 ...
- SQL 分页查询语句大全即（查找第N到M条记录的方法）
SQL 分页查询语句大全即(查找第N到M条记录的方法) 第一种方法,我的原创方法 row=2 表示分页行数 page=1 表示页码 getnum=row*page select * from (se ...
- oracle查询语句大全
oracle查询语句大全 oracle 基本命令大全一 1.create user username identified by password;//建用户名和密码oracle ,oracle 2. ...
- MySQL语句系列--查询语句大全(有示例)
原文网址:MySQL语句系列--查询语句大全(有示例)_IT利刃出鞘的博客-CSDN博客 简介 本文介绍MySQL查询语法(命令)的使用.包括:基本查询.运算条件.排序.分页.分组.联结.组合.子查询 ...
最新文章
- 关于 java.toString() ,(String),String.valueOf的区别
- android 热修复视频,Android热修复
- 技术实践丨Prometheus+Grafana搭建HBase监控仪表盘
- python数据加载常规教程_Python加载数据的5种不同方式(收藏)
- could not extract ResultSet/could not execute statement
- 第四周任务：wordCountPro
- 简单的form表单操作(Servlet)
- HTML fieldset 标签
- Allegro PCB 封装库
- WinCC vbs脚本小结
- 计算机表格的条件公式,电脑Excel输出时如何进行多条件判断
- java中dl列表_Java DLFolder.getFolderId方法代码示例
- 软件测试分类、分级与软件缺陷管理
- 采集本地HTML数据,火车采集器保存为本地word、excel、html、txt文件方法及文件模板_爬虫软件技术与爬虫软件网页数据采集器门户...
- 面对电车难题，自动驾驶会怎么选？
- 将.m3u8转换为视频文件保存到本地
- YY游戏云平台在AngularJS上的实践总结
- 在vue/html中添加单选按钮
- 计算机专业的研究生专业方向
- python数据分析知识体系_数据分析知识体系