PostgreSQL 9.3.2 Documentation
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# 9.20. Aggregate Functions

Aggregate functions compute a single result from a set of input values. The built-in aggregate functions are listed in Table 9-46 and Table 9-47. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Table 9-46. General-Purpose Aggregate Functions

Function | Argument Type(s) | Return Type | Description
--- | --- | --- | ---
`array_agg(`expression`)` | any | array of the argument type | input values, including nulls, 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`
`json_agg(`record`)` | `record` | `json` | aggregates records as a JSON array of objects
`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
`string_agg(`expression`, `delimiter`)` | (`text`, `text`) or (`bytea`, `bytea`) | same as argument types | input values concatenated into a string, separated by delimiter
`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 subquery 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 require effort proportional to the size of the table: PostgreSQL will need to scan either the entire table or the entirety of an index which includes all rows in the table.

The aggregate functions `array_agg`, `json_agg`, `string_agg`, and `xmlagg`, as well as similar user-defined aggregate functions, produce meaningfully different result values depending on the order of the input values. This ordering is unspecified by default, but can be controlled by writing an `ORDER BY` clause within the aggregate call, as shown in Section 4.2.7. Alternatively, supplying the input values from a sorted subquery will usually work. 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.

Table 9-47 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 9-47. 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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