# 9.15. Aggregate Functions

Aggregate functions compute a single result value from a set of input values. Table 9-43 shows the built-in aggregate functions. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Table 9-43. Aggregate Functions

FunctionArgument TypeReturn TypeDescription
`avg(expression)` smallint, integer, bigint, real, double precision, numeric, or interval numeric for any integer type argument, double precision for a floating-point argument, otherwise the same as the argument data type the average (arithmetic mean) of all input values
`count(*)` bigintnumber of input values
`count(expression)`anybigint number of input values for which the value of expression is not null
`max(expression)`any numeric, string, or date/time typesame as argument type maximum value of expression across all input values
`min(expression)`any numeric, string, or date/time typesame as argument type minimum value of expression across all input values
`stddev(expression)` smallint, integer, bigint, real, double precision, or numeric double precision for floating-point arguments, otherwise numeric sample standard deviation of the input values
`sum(expression)` smallint, integer, bigint, real, double precision, numeric, or interval bigint for smallint or integer arguments, numeric for bigint arguments, double precision for floating-point arguments, otherwise the same as the argument data type sum of expression across all input values
`variance`(expression) smallint, integer, bigint, real, double precision, or numeric double precision for floating-point arguments, otherwise numeric sample variance of the input values (square of the sample standard deviation)

It should be noted that except for `count`, these functions return a null value when no rows are selected. In particular, `sum` of no rows returns null, not zero as one might expect. The function `coalesce` may be used to substitute zero for null when necessary.

Note: Users accustomed to working with other SQL database management systems may be surprised by the performance characteristics of certain aggregate functions in PostgreSQL when the aggregate is applied to the entire table (in other words, no WHERE clause is specified). In particular, a query like

`SELECT min(col) FROM sometable;`

will be executed by PostgreSQL using a sequential scan of the entire table. Other database systems may optimize queries of this form to use an index on the column, if one is available. Similarly, the aggregate functions `max()` and `count()` always require a sequential scan if applied to the entire table in PostgreSQL.

PostgreSQL cannot easily implement this optimization because it also allows for user-defined aggregate queries. Since `min()`, `max()`, and `count()` are defined using a generic API for aggregate functions, there is no provision for special-casing the execution of these functions under certain circumstances.

Fortunately, there is a simple workaround for `min()` and `max()`. The query shown below is equivalent to the query above, except that it can take advantage of a B-tree index if there is one present on the column in question.

`SELECT col FROM sometable ORDER BY col ASC LIMIT 1;`

A similar query (obtained by substituting DESC for ASC in the query above) can be used in the place of `max()`).

Unfortunately, there is no similarly trivial query that can be used to improve the performance of `count()` when applied to the entire table.