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PostgreSQL 8.2.3 Documentation
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9.15. Aggregate Functions

Aggregate functions compute a single result value from a set of input values. The built-in aggregate functions are listed in Table 9-37 and Table 9-38. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Table 9-37. General-Purpose Aggregate Functions

Function	Argument Type	Return Type	Description
`avg(expression)`	`smallint`, `int`, `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
`bit_and(expression)`	`smallint`, `int`, `bigint`, or `bit`	same as argument data type	the bitwise AND of all non-null input values, or null if none
`bit_or(expression)`	`smallint`, `int`, `bigint`, or `bit`	same as argument data type	the bitwise OR of all non-null input values, or null if none
`bool_and(expression)`	`bool`	`bool`	true if all input values are true, otherwise false
`bool_or(expression)`	`bool`	`bool`	true if at least one input value is true, otherwise false
`count(*)`		`bigint`	number of input rows
`count(expression)`	any	`bigint`	number of input rows for which the value of `expression` is not null
`every(expression)`	`bool`	`bool`	equivalent to `bool_and`
`max(expression)`	any array, numeric, string, or date/time type	same as argument type	maximum value of `expression` across all input values
`min(expression)`	any array, numeric, string, or date/time type	same as argument type	minimum value of `expression` across all input values
`sum(expression)`	`smallint`, `int`, `bigint`, `real`, `double precision`, `numeric`, or `interval`	`bigint` for `smallint` or `int` 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

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 coalesce function may be used to substitute zero for null when necessary.

Note: Boolean aggregates bool_and and bool_or correspond to standard SQL aggregates every and any or some. As for any and some, it seems that there is an ambiguity built into the standard syntax:
SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;
Here ANY can be considered both as leading to a subquery or as an aggregate if the select expression returns 1 row. Thus the standard name cannot be given to these aggregates.

Note: Users accustomed to working with other SQL database management systems may be surprised by the performance of the count aggregate when it is applied to the entire table. A query like:
SELECT count(*) FROM sometable;
will be executed by PostgreSQL using a sequential scan of the entire table.

Table 9-38 shows aggregate functions typically used in statistical analysis. (These are separated out merely to avoid cluttering the listing of more-commonly-used aggregates.) Where the description mentions N, it means the number of input rows for which all the input expressions are non-null. In all cases, null is returned if the computation is meaningless, for example when N is zero.

Table 9-38. Aggregate Functions for Statistics

Function	Argument Type	Return Type	Description
`corr(Y, X)`	`double precision`	`double precision`	correlation coefficient
`covar_pop(Y, X)`	`double precision`	`double precision`	population covariance
`covar_samp(Y, X)`	`double precision`	`double precision`	sample covariance
`regr_avgx(Y, X)`	`double precision`	`double precision`	average of the independent variable (`sum(X)/N`)
`regr_avgy(Y, X)`	`double precision`	`double precision`	average of the dependent variable (`sum(Y)/N`)
`regr_count(Y, X)`	`double precision`	`bigint`	number of input rows in which both expressions are nonnull
`regr_intercept(Y, X)`	`double precision`	`double precision`	y-intercept of the least-squares-fit linear equation determined by the (`X`, `Y`) pairs
`regr_r2(Y, X)`	`double precision`	`double precision`	square of the correlation coefficient
`regr_slope(Y, X)`	`double precision`	`double precision`	slope of the least-squares-fit linear equation determined by the (`X`, `Y`) pairs
`regr_sxx(Y, X)`	`double precision`	`double precision`	`sum(X^2) - sum(X)^2/N` ("sum of squares" of the independent variable)
`regr_sxy(Y, X)`	`double precision`	`double precision`	`sum(XY) - sum(X) sum(Y)/N` ("sum of products" of independent times dependent variable)
`regr_syy(Y, X)`	`double precision`	`double precision`	`sum(Y^2) - sum(Y)^2/N` ("sum of squares" of the dependent variable)
`stddev(expression)`	`smallint`, `int`, `bigint`, `real`, `double precision`, or `numeric`	`double precision` for floating-point arguments, otherwise `numeric`	historical alias for `stddev_samp`
`stddev_pop(expression)`	`smallint`, `int`, `bigint`, `real`, `double precision`, or `numeric`	`double precision` for floating-point arguments, otherwise `numeric`	population standard deviation of the input values
`stddev_samp(expression)`	`smallint`, `int`, `bigint`, `real`, `double precision`, or `numeric`	`double precision` for floating-point arguments, otherwise `numeric`	sample standard deviation of the input values
`variance`(`expression`)	`smallint`, `int`, `bigint`, `real`, `double precision`, or `numeric`	`double precision` for floating-point arguments, otherwise `numeric`	historical alias for `var_samp`
`var_pop`(`expression`)	`smallint`, `int`, `bigint`, `real`, `double precision`, or `numeric`	`double precision` for floating-point arguments, otherwise `numeric`	population variance of the input values (square of the population standard deviation)
`var_samp`(`expression`)	`smallint`, `int`, `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)

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