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(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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