Aggregate Functions
| PostgreSQL 9.4.26 Documentation | |||
|---|---|---|---|
| Prev | Up | Chapter 9. Functions and Operators | Next |
Aggregate functions compute a single result from a set of input values. The built-in normal aggregate functions are listed in Table 9-49 and Table 9-50 . The built-in ordered-set aggregate functions are listed in Table 9-51 and Table 9-52 . The special syntax considerations for aggregate functions are explained in Section 4.2.7 . Consult Section 2.7 for additional introductory information.
Table 9-49. 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 non-null 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(
expression
)
|
any | json | aggregates values, including nulls, as a JSON array |
json_object_agg(
name
,
value
)
|
(any, any) | json | aggregates name/value pairs as a JSON object; values can be null, but not names |
max(
expression
)
|
any array, numeric, string, or date/time type | same as argument type | maximum value of expression across all non-null input values |
min(
expression
)
|
any array, numeric, string, or date/time type | same as argument type | minimum value of expression across all non-null input values |
string_agg(
expression
,
delimiter
)
|
( text , text ) or ( bytea , bytea ) | same as argument types | non-null input values concatenated into a string, separated by delimiter |
sum(
expression
)
|
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 non-null input values |
xmlagg(
expression
)
|
xml | xml | concatenation of non-null 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_andandbool_orcorrespond to standard SQL aggregateseveryandanyorsome. As foranyandsome, it seems that there is an ambiguity built into the standard syntax:SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;Here
ANYcan 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
countaggregate 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
,
json_object_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 9-50 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 9-50. 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) |
Table 9-51 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as "inverse distribution" functions.
Table 9-51. Ordered-Set Aggregate Functions
| Function | Direct Argument Type(s) | Aggregated Argument Type(s) | Return Type | Description |
|---|---|---|---|---|
mode() WITHIN GROUP (ORDER BY
sort_expression
)
|
any sortable type | same as sort expression | returns the most frequent input value (arbitrarily choosing the first one if there are multiple equally-frequent results) | |
percentile_cont(
fraction
) WITHIN GROUP (ORDER BY
sort_expression
)
|
double precision | double precision or interval | same as sort expression | continuous percentile: returns a value corresponding to the specified fraction in the ordering, interpolating between adjacent input items if needed |
percentile_cont(
fractions
) WITHIN GROUP (ORDER BY
sort_expression
)
|
double precision[] | double precision or interval | array of sort expression's type | multiple continuous percentile: returns an array of results matching the shape of the fractions parameter, with each non-null element replaced by the value corresponding to that percentile |
percentile_disc(
fraction
) WITHIN GROUP (ORDER BY
sort_expression
)
|
double precision | any sortable type | same as sort expression | discrete percentile: returns the first input value whose position in the ordering equals or exceeds the specified fraction |
percentile_disc(
fractions
) WITHIN GROUP (ORDER BY
sort_expression
)
|
double precision[] | any sortable type | array of sort expression's type | multiple discrete percentile: returns an array of results matching the shape of the fractions parameter, with each non-null element replaced by the input value corresponding to that percentile |
All the aggregates listed in Table 9-51 ignore null values in their sorted input. For those that take a fraction parameter, the fraction value must be between 0 and 1; an error is thrown if not. However, a null fraction value simply produces a null result.
Each of the aggregates listed in Table 9-52 is associated with a window function of the same name defined in Section 9.21 . In each case, the aggregate result is the value that the associated window function would have returned for the "hypothetical" row constructed from args , if such a row had been added to the sorted group of rows computed from the sorted_args .
Table 9-52. Hypothetical-Set Aggregate Functions
| Function | Direct Argument Type(s) | Aggregated Argument Type(s) | Return Type | Description |
|---|---|---|---|---|
rank(
args
) WITHIN GROUP (ORDER BY
sorted_args
)
|
VARIADIC "any" | VARIADIC "any" | bigint | rank of the hypothetical row, with gaps for duplicate rows |
dense_rank(
args
) WITHIN GROUP (ORDER BY
sorted_args
)
|
VARIADIC "any" | VARIADIC "any" | bigint | rank of the hypothetical row, without gaps |
percent_rank(
args
) WITHIN GROUP (ORDER BY
sorted_args
)
|
VARIADIC "any" | VARIADIC "any" | double precision | relative rank of the hypothetical row, ranging from 0 to 1 |
cume_dist(
args
) WITHIN GROUP (ORDER BY
sorted_args
)
|
VARIADIC "any" | VARIADIC "any" | double precision | relative rank of the hypothetical row, ranging from 1/ N to 1 |
For each of these hypothetical-set aggregates, the list of direct arguments given in args must match the number and types of the aggregated arguments given in sorted_args . Unlike most built-in aggregates, these aggregates are not strict, that is they do not drop input rows containing nulls. Null values sort according to the rule specified in the ORDER BY clause.