Aggregate Functions

PostgreSQL 8.4.21 Documentation
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# 9.18. Aggregate Functions

Aggregate functions compute a single result from a set of input values. The built-in aggregate functions are listed in Table 9-42 and Table 9-43. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Table 9-42. General-Purpose Aggregate Functions

Function | Argument Type | Return Type | Description
--- | --- | --- | ---
`array_agg(`expression`)` | any | array of the argument type | input values concatenated into an array
`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
`xmlagg(`expression`)` | `xml` | `xml` | concatenation of XML values (see also Section 9.14.1.7)

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, and `array_agg` returns null rather than an empty array when there are no input rows. The `coalesce` function can be used to substitute zero or an empty array 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 either as introducing a subquery, or as being an aggregate function, if the sub-select returns one row with a boolean value. Thus the standard name cannot be given to these aggregates.

> Note: Users accustomed to working with other SQL database management systems might be disappointed 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.

The aggregate functions `array_agg` and `xmlagg`, as well as similar user-defined aggregate functions, produce meaningfully different result values depending on the order of the input values. In the current implementation, the order of the input is in principle unspecified. Supplying the input values from a sorted subquery will usually work, however. For example:

    SELECT xmlagg(x) FROM (SELECT x FROM test ORDER BY y DESC) AS tab;

But this syntax is not allowed in the SQL standard, and is not portable to other database systems. A future version of PostgreSQL might provide an additional feature to control the order in a better-defined way (`xmlagg(expr ORDER BY expr, expr, ...)`).

Table 9-43 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 9-43. 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(`X`*`Y`) - 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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