impala GROUP BY子句

由 jojocool 创建，youj 最后一次修改 2016-12-19 Impala GROUP BY子句与SELECT语句协作使用，以将相同的数据排列到组中。 语法以下是GROUP BY子句的语法。 select data from table_name Group BY col_name;例假设我们在数据库my_db中有一个名为customers的表，其内容如下 - [quickstart.cloudera:21000] > select * from customers; Query: select * from customers +----+----------+-----+-----------+--------+ | id | name | age | address | salary | +----+----------+-----+-----------+--------+ | 1 | Ramesh | 32 | Ahmedabad | 20000 | | 2 | Khilan | 25 | Delhi | 15000 | | 3 | kaushik | 23 | Kota | 30000 | | 4 | Chaitali | 25 | Mumbai | 35000 | | 5 | Hardik | 27 | Bhopal | 40000 | | 6 | Komal | 22 | MP | 32000 | +----+----------+-----+-----------+--------+ Fetched 6 row(s) in 0.51s您可以使用GROUP BY查询获得每个客户的工资总额，如下所示。 [quickstart.cloudera:21000] > Select name, sum(salary) from customers Group BY name;执行时，上述查询给出以下输出。 Query: select name, sum(salary) from customers Group BY name +----------+-------------+ | name | sum(salary) | +----------+-------------+ | Ramesh | 20000 | | Komal | 32000 | | Hardik | 40000 | | Khilan | 15000 | | Chaitali | 35000 | | kaushik | 30000 |+----------+-------------+ Fetched 6 row(s) in 1.75s假设此表有多个记录，如下所示。 +----+----------+-----+-----------+--------+ | id | name | age | address | salary | +----+----------+-----+-----------+--------+ | 1 | Ramesh | 32 | Ahmedabad | 20000 || 2 | Ramesh | 32 | Ahmedabad | 1000| | | 3 | Khilan | 25 | Delhi | 15000 | | 4 | kaushik | 23 | Kota | 30000 | | 5 | Chaitali | 25 | Mumbai | 35000 || 6 | Chaitali | 25 | Mumbai | 2000 || 7 | Hardik | 27 | Bhopal | 40000 | | 8 | Komal | 22 | MP | 32000 | +----+----------+-----+-----------+--------+现在，您可以使用Group By子句，如下所示，考虑重复的记录条目，获取员工的总工资。 Select name, sum(salary) from customers Group BY name;执行时，上述查询给出以下输出。 Query: select name, sum(salary) from customers Group BY name +----------+-------------+ | name | sum(salary) | +----------+-------------+ | Ramesh | 21000 | | Komal | 32000 | | Hardik | 40000 | | Khilan | 15000 | | Chaitali | 37000 | | kaushik | 30000 | +----------+-------------+Fetched 6 row(s) in 1.75s