Use partitioning in the OVER clause of the aggregate-analytical function like this : PARTITION « Analytical Functions « Oracle PL / SQL

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Oracle PL / SQL » Analytical Functions » PARTITION 
Use partitioning in the OVER clause of the aggregate-analytical function like this
 


SQL> -- create demo table
SQL> create table Employee(
  2    empno              Number(3)  NOT NULL, -- Employee ID
  3    ename              VARCHAR2(10 BYTE),   -- Employee Name
  4    hireDate          DATE,                -- Date Employee Hired
  5    orig_salary        Number(8,2),         -- Orignal Salary
  6    curr_salary        Number(8,2),         -- Current Salary
  7    region             VARCHAR2(BYTE)     -- Region where employeed
  8  )
  9  /

Table created.

SQL>
SQL> create table job(
  2    empno              Number(3)  NOT NULL, -- Employee ID
  3    jobtitle           VARCHAR2(10 BYTE)    -- Employee job title
  4  )
  5  /

SQL> -- prepare data for employee table
SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(122,'Alison',to_date('19960321','YYYYMMDD'), 45000,       48000,       'E')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(123'James',to_date('19781212','YYYYMMDD'), 23000,       32000,       'W')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(104,'Celia',to_date('19821024','YYYYMMDD'), 53000,       58000,        'E')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(105,'Robert',to_date('19840115','YYYYMMDD'), 31000,      36000,        'W')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(116,'Linda', to_date('19870730','YYYYMMDD'), 43000,       53000,       'E')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(117,'David', to_date('19901231','YYYYMMDD'), 78000,       85000,       'W')
  3  /

row created.

SQL> insert into Employee(empno,  ename,  hireDate,                       orig_salary, curr_salary, region)
  2                values(108,'Jode',  to_date('19960917','YYYYMMDD'), 21000,       29000,       'E')
  3  /

row created.

SQL>
SQL> select from employee;

     EMPNO ENAME      HIREDATE  ORIG_SALARY CURR_SALARY R
---------- ---------- --------- ----------- ----------- -
       122 Alison     21-MAR-96       45000       48000 E
       123 James      12-DEC-78       23000       32000 W
       104 Celia      24-OCT-82       53000       58000 E
       105 Robert     15-JAN-84       31000       36000 W
       116 Linda      30-JUL-87       43000       53000 E
       117 David      31-DEC-90       78000       85000 W
       108 Jode       17-SEP-96       21000       29000 E

rows selected.

SQL>
SQL> -- Use partitioning in the OVER clause of the aggregate-analytical function like this:
SQL>
SQL> SELECT empno, ename, orig_salary, region,
  2    ROUND(AVG(orig_salaryOVER(PARTITION BY region))
  3        "Avg. Salary"
  4  FROM employee
  5  ORDER BY region, ename;

     EMPNO ENAME      ORIG_SALARY R Avg. Salary
---------- ---------- ----------- - -----------
       122 Alison           45000 E       40500
       104 Celia            53000 E       40500
       108 Jode             21000 E       40500
       116 Linda            43000 E       40500
       117 David            78000 W       44000
       123 James            23000 W       44000
       105 Robert           31000 W       44000

rows selected.

SQL>
SQL>
SQL> -- clean the table
SQL> drop table Employee
  2  /

Table dropped.

SQL>
SQL>
           
         
  
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7. count(*) over partition by, order by and range unbounded preceding
8. dense_rank() over partition by, order by
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12. SPREADSHEET PARTITION BY
13. sum salary over PARTITION BY
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