Aggregate functions compute a single result from a set of input values. The built-in general-purpose aggregate functions are listed in Table 9.57 while statistical aggregates are in Table 9.58. The built-in within-group ordered-set aggregate functions are listed in Table 9.59 while the built-in within-group hypothetical-set 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. General-Purpose 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 non-null input values. | Yes |
Computes the bitwise AND of all non-null input values. | Yes |
Computes the bitwise OR of all non-null input values. | Yes |
Computes the bitwise exclusive OR of all non-null input values. Can be useful as a checksum for an unordered set of values. | Yes |
Returns true if all non-null input values are true, otherwise false. | Yes |
|
Returns true if any non-null 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 non-null input
values. Available for any numeric, string, date/time, or enum type,
as well as | Yes |
Computes the minimum of the non-null input
values. Available for any numeric, string, date/time, or enum type,
as well as | Yes |
Computes the union of the non-null input values. | No |
Computes the intersection of the non-null input values. | No |
Concatenates the non-null input values into a string. Each value
after the first is preceded by the
corresponding | No |
|
Computes the sum of the non-null input values. | Yes |
|
Concatenates the non-null 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 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;
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.
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.
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 more-commonly-used 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 non-null.
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 ordered-set 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. Ordered-Set Aggregate Functions
Each of the “hypothetical-set” 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 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.
Table 9.60. Hypothetical-Set 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 next-to-last 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).