10th November 2022:
PostgreSQL 15.1, 14.6, 13.9, 12.13, 11.18, and 10.23 Released!

Development Versions:
devel

This documentation is for an unsupported version of PostgreSQL.

You may want to view the same page for the current version, or one of the other supported versions listed above instead.

You may want to view the same page for the current version, or one of the other supported versions listed above instead.

PostgreSQL 9.0.23 Documentation | ||||
---|---|---|---|---|

Prev | Up | Chapter 9. Functions and Operators | Next |

*Aggregate functions* compute a single
result from a set of input values. The built-in aggregate
functions are listed in Table
9-43 and Table
9-44. 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. General-Purpose Aggregate Functions

Function | Argument Type(s) | Return Type | Description |
---|---|---|---|

`array_agg(` |
any | array of the argument type | input values, including nulls, concatenated into an array |

`avg(` |
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(` |
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(` |
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(` |
bool |
bool |
true if all input values are true, otherwise false |

`bool_or(` |
bool |
bool |
true if at least one input value is true, otherwise false |

`count(*)` |
bigint |
number of input rows | |

`count(` |
any | bigint |
number of input rows for which the value of
expression is not
null |

`every(` |
bool |
bool |
equivalent to `bool_and` |

`max(` |
any array, numeric, string, or date/time type | same as argument type | maximum value of expression across all input
values |

`min(` |
any array, numeric, string, or date/time type | same as argument type | minimum value of expression across all input
values |

`string_agg(` |
text, text |
text |
input values concatenated into a string, separated by delimiter |

`sum(` |
smallint, int, bigint, real, double precision,
numeric, interval, or money |
bigint for smallint or int
arguments, numeric for bigint arguments, otherwise the same as the
argument data type |
sum of expression
across all input values |

`xmlagg(` |
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 be executed by PostgreSQL using a sequential scan of the entire table.

The aggregate functions `array_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
9-44 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 9-44. Aggregate Functions for Statistics

Function | Argument Type | Return Type | Description |
---|---|---|---|

`corr(` |
double precision |
double precision |
correlation coefficient |

`covar_pop(` |
double precision |
double precision |
population covariance |

`covar_samp(` |
double precision |
double precision |
sample covariance |

`regr_avgx(` |
double precision |
double precision |
average of the independent variable (sum()X)/N |

`regr_avgy(` |
double precision |
double precision |
average of the dependent variable (sum()Y)/N |

`regr_count(` |
double precision |
bigint |
number of input rows in which both expressions are nonnull |

`regr_intercept(` |
double precision |
double precision |
y-intercept of the least-squares-fit linear equation
determined by the (X,
Y) pairs |

`regr_r2(` |
double precision |
double precision |
square of the correlation coefficient |

`regr_slope(` |
double precision |
double precision |
slope of the least-squares-fit linear equation
determined by the (X,
Y) pairs |

`regr_sxx(` |
double precision |
double precision |
sum( ("sum of
squares" of the independent variable)X^2) - sum(X)^2/N |

`regr_sxy(` |
double precision |
double precision |
sum( ("sum of
products" of independent times dependent
variable)X*Y) - sum(X) * sum(Y)/N |

`regr_syy(` |
double precision |
double precision |
sum( ("sum of
squares" of the dependent variable)Y^2) - sum(Y)^2/N |

`stddev(` |
smallint, int, bigint, real, double precision,
or numeric |
double precision for
floating-point arguments, otherwise numeric |
historical alias for `stddev_samp` |

`stddev_pop(` |
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(` |
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) |