# RANK

Aggregate Syntax

rank_aggregate::=

Description of the illustration rank_aggregate.gif

Analytic Syntax

rank_analytic::=

Description of the illustration rank_analytic.gif

"Analytic Functions" for information on syntax, semantics, and restrictions

Purpose

`RANK` calculates the rank of a value in a group of values. The return type is `NUMBER`.

Table 2-10, "Implicit Type Conversion Matrix" for more information on implicit conversion and "Numeric Precedence" for information on numeric precedence

Rows with equal values for the ranking criteria receive the same rank. Oracle Database then adds the number of tied rows to the tied rank to calculate the next rank. Therefore, the ranks may not be consecutive numbers. This function is useful for top-N and bottom-N reporting.

• As an aggregate function, `RANK` calculates the rank of a hypothetical row identified by the arguments of the function with respect to a given sort specification. The arguments of the function must all evaluate to constant expressions within each aggregate group, because they identify a single row within each group. The constant argument expressions and the expressions in the `ORDER` `BY` clause of the aggregate match by position. Therefore, the number of arguments must be the same and their types must be compatible.

• As an analytic function, `RANK` computes the rank of each row returned from a query with respect to the other rows returned by the query, based on the values of the `value_exprs` in the `order_by_clause`.

Aggregate Example

The following example calculates the rank of a hypothetical employee in the sample table `hr.employees` with a salary of \$15,500 and a commission of 5%:

```SELECT RANK(15500, .05) WITHIN GROUP
(ORDER BY salary, commission_pct) "Rank"
FROM employees;

Rank
----------
105

```

Similarly, the following query returns the rank for a \$15,500 salary among the employee salaries:

```SELECT RANK(15500) WITHIN GROUP
(ORDER BY salary DESC) "Rank of 15500"
FROM employees;

Rank of 15500
--------------
4
```

Analytic Example

The following statement ranks the employees in the sample `hr` schema in department 80 based on their salary and commission. Identical salary values receive the same rank and cause nonconsecutive ranks. Compare this example with the example for DENSE_RANK.

```SELECT department_id, last_name, salary, commission_pct,
RANK() OVER (PARTITION BY department_id
ORDER BY salary DESC, commission_pct) "Rank"
FROM employees WHERE department_id = 80;

DEPARTMENT_ID LAST_NAME                     SALARY COMMISSION_PCT       Rank
------------- ------------------------- ---------- -------------- ----------
80 Russell                        14000             .4          1
80 Partners                       13500             .3          2
80 Errazuriz                      12000             .3          3
80 Ozer                           11500            .25          4
80 Cambrault                      11000             .3          5
80 Abel                           11000             .3          5
80 Zlotkey                        10500             .2          7
80 Vishney                        10500            .25          8
80 Bloom                          10000             .2          9
80 Tucker                         10000             .3         10
80 King                           10000            .35         11
80 Fox                             9600             .2         12
80 Greene                          9500            .15         13
80 Bernstein                       9500            .25         14
80 Sully                           9500            .35         15
80 Hall                            9000            .25         16
80 McEwen                          9000            .35         17
80 Hutton                          8800            .25         18
80 Taylor                          8600             .2         19
80 Livingston                      8400             .2         20
80 Olsen                           8000             .2         21
80 Smith                           8000             .3         22
80 Cambrault                       7500             .2         23
80 Doran                           7500             .3         24
80 Smith                           7400            .15         25
80 Bates                           7300            .15         26
80 Marvins                         7200             .1         27
80 Tuvault                         7000            .15         28
80 Sewall                          7000            .25         29
80 Lee                             6800             .1         30
80 Ande                            6400             .1         31
80 Banda                           6200             .1         32
80 Johnson                         6200             .1         32
80 Kumar                           6100             .1         34
```