9.21. Aggregate Functions
Aggregate functions compute a single result from a set of input values. The builtin generalpurpose aggregate functions are listed in Table 9.57 while statistical aggregates are in Table 9.58 . The builtin withingroup orderedset aggregate functions are listed in Table 9.59 while the builtin withingroup hypotheticalset ones are in Table 9.60 . Grouping operations, which are closely related to aggregate functions, are listed in Table 9.61 . The special syntax considerations for aggregate functions are explained in Section 4.2.7 . Consult Section 2.7 for additional introductory information.
Aggregate functions that support Partial Mode are eligible to participate in various optimizations, such as parallel aggregation.
Table 9.57. GeneralPurpose Aggregate Functions
Function Description 
Partial Mode 

Collects all the input values, including nulls, into an array. 
No 
Concatenates all the input arrays into an array of one higher dimension. (The inputs must all have the same dimensionality, and cannot be empty or null.) 
No 
Computes the average (arithmetic mean) of all the nonnull input values. 
Yes 
Computes the bitwise AND of all nonnull input values. 
Yes 
Computes the bitwise OR of all nonnull input values. 
Yes 
Computes the bitwise exclusive OR of all nonnull input values. Can be useful as a checksum for an unordered set of values. 
Yes 
Returns true if all nonnull input values are true, otherwise false. 
Yes 
Returns true if any nonnull input value is true, otherwise false. 
Yes 
Computes the number of input rows. 
Yes 
Computes the number of input rows in which the input value is not null. 
Yes 
This is the SQL standard's equivalent to

Yes 
Collects all the input values, including nulls, into a JSON array.
Values are converted to JSON as per

No 
Collects all the key/value pairs into a JSON object. Key arguments
are coerced to text; value arguments are converted as
per

No 
Computes the maximum of the nonnull input
values. Available for any numeric, string, date/time, or enum type,
as well as

Yes 
Computes the minimum of the nonnull input
values. Available for any numeric, string, date/time, or enum type,
as well as

Yes 
Computes the union of the nonnull input values. 
No 
Computes the intersection of the nonnull input values. 
No 
Concatenates the nonnull input values into a string. Each value
after the first is preceded by the
corresponding

No 
Computes the sum of the nonnull input values. 
Yes 
Concatenates the nonnull XML input values (see Section 9.15.1.7 ). 
No 
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.
The aggregate functions
array_agg
,
json_agg
,
jsonb_agg
,
json_object_agg
,
jsonb_object_agg
,
string_agg
,
and
xmlagg
, as well as similar userdefined
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;
Beware that this approach can fail if the outer query level contains additional processing, such as a join, because that might cause the subquery's output to be reordered before the aggregate is computed.
Note
The boolean aggregates
bool_and
and
bool_or
correspond to the standard SQL aggregates
every
and
any
or
some
.
PostgreSQL
supports
every
, but not
any
or
some
, because 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 that includes all rows in the table.
Table 9.58
shows
aggregate functions typically used in statistical analysis.
(These are separated out merely to avoid cluttering the listing
of morecommonlyused aggregates.) Functions shown as
accepting
numeric_type
are available for all
the types
smallint
,
integer
,
bigint
,
numeric
,
real
,
and
double precision
.
Where the description mentions
N
, it means the
number of input rows for which all the input expressions are nonnull.
In all cases, null is returned if the computation is meaningless,
for example when
N
is zero.
Table 9.58. Aggregate Functions for Statistics
Table 9.59
shows some
aggregate functions that use the
orderedset aggregate
syntax. These functions are sometimes referred to as
"
inverse
distribution
"
functions. Their aggregated input is introduced by
ORDER BY
, and they may also take a
direct
argument
that is not aggregated, but is computed only once.
All these functions ignore null values in their aggregated 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.
Table 9.59. OrderedSet Aggregate Functions
Each of the
"
hypotheticalset
"
aggregates listed in
Table 9.60
is associated with a
window function of the same name defined in
Section 9.22
. In each case, the aggregate's 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 represented by the
sorted_args
.
For each of these functions, the list of direct arguments
given in
args
must match the number and types of
the aggregated arguments given in
sorted_args
.
Unlike most builtin 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.
Table 9.60. HypotheticalSet Aggregate Functions
Table 9.61. Grouping Operations
The grouping operations shown in
Table 9.61
are used in conjunction with
grouping sets (see
Section 7.2.4
) to distinguish
result rows. The arguments to the
GROUPING
function
are not actually evaluated, but they must exactly match expressions given
in the
GROUP BY
clause of the associated query level.
For example:
=>
SELECT * FROM items_sold;
make  model  sales ++ Foo  GT  10 Foo  Tour  20 Bar  City  15 Bar  Sport  5 (4 rows)=>
SELECT make, model, GROUPING(make,model), sum(sales) FROM items_sold GROUP BY ROLLUP(make,model);
make  model  grouping  sum +++ Foo  GT  0  10 Foo  Tour  0  20 Bar  City  0  15 Bar  Sport  0  5 Foo   1  30 Bar   1  20   3  50 (7 rows)
Here, the
grouping
value
0
in the
first four rows shows that those have been grouped normally, over both the
grouping columns. The value
1
indicates
that
model
was not grouped by in the nexttolast two
rows, and the value
3
indicates that
neither
make
nor
model
was grouped
by in the last row (which therefore is an aggregate over all the input
rows).