*Aggregate functions* compute a single result
from a set of input values. The built-in general-purpose aggregate
functions are listed in 표 9.52
and statistical aggregates in 표 9.53.
The built-in within-group ordered-set aggregate functions
are listed in 표 9.54
while the built-in within-group hypothetical-set ones are in 표 9.55. Grouping operations,
which are closely related to aggregate functions, are listed in
표 9.56.
The special syntax considerations for aggregate
functions are explained in 4.2.7절.
Consult 2.7절 for additional introductory
information.

**표 9.52. General-Purpose Aggregate Functions**

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

`array_agg(`
| any non-array type | array of the argument type | No | input values, including nulls, concatenated into an array |

`array_agg(`
| any array type | same as argument data type | No | input arrays concatenated into array of one higher dimension (inputs must all have same dimensionality, and cannot be empty or NULL) |

`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
| Yes | the average (arithmetic mean) of all input values |

`bit_and(`
| `smallint` , `int` , `bigint` , or
`bit`
| same as argument data type | Yes | 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 | Yes | the bitwise OR of all non-null input values, or null if none |

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

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

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

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

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

`json_agg(`
| `any`
| `json`
| No | aggregates values as a JSON array |

`jsonb_agg(`
| `any`
| `jsonb`
| No | aggregates values as a JSON array |

`json_object_agg(`
| `(any, any)`
| `json`
| No | aggregates name/value pairs as a JSON object |

`jsonb_object_agg(`
| `(any, any)`
| `jsonb`
| No | aggregates name/value pairs as a JSON object |

`max(`
| any numeric, string, date/time, network, or enum type, or arrays of these types | same as argument type | Yes | maximum value of across all input
values
`expression` |

`min(`
| any numeric, string, date/time, network, or enum type, or arrays of these types | same as argument type | Yes | minimum value of across all input
values
`expression` |

` string_agg(`
| (`text` , `text` ) or (`bytea` , `bytea` )
| same as argument types | No | 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
| Yes | sum of across all input values`expression` |

`xmlagg(`
| `xml`
| `xml`
| No | concatenation of XML values (see also 9.14.1.7절) |

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.

Aggregate functions which support *Partial Mode*
are eligible to participate in various optimizations, such as parallel
aggregation.

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.

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 which includes all rows in the table.

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
4.2.7절.
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.

표 9.53 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`

**표 9.53. Aggregate Functions for Statistics**

표 9.54 shows some
aggregate functions that use the *ordered-set aggregate*
syntax. These functions are sometimes referred to as “inverse
distribution” functions.

**표 9.54. Ordered-Set Aggregate Functions**

All the aggregates listed in 표 9.54
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
표 9.55 is associated with a
window function of the same name defined in
9.21절. 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`

**표 9.55. Hypothetical-Set Aggregate Functions**

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`

`ORDER BY`

clause.
**표 9.56. Grouping Operations**

Grouping operations are used in conjunction with grouping sets (see
7.2.4절) to distinguish result rows. The
arguments to the `GROUPING`

operation are not actually evaluated,
but they must match exactly expressions given in the `GROUP BY`

clause of the associated query level. Bits are assigned with the rightmost
argument being the least-significant bit; each bit is 0 if the corresponding
expression is included in the grouping criteria of the grouping set generating
the result row, and 1 if it is not. For example:

`=>`

make | model | sales -------+-------+------- Foo | GT | 10 Foo | Tour | 20 Bar | City | 15 Bar | Sport | 5 (4 rows)`SELECT * FROM items_sold;`

`=>`

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)`SELECT make, model, GROUPING(make,model), sum(sales) FROM items_sold GROUP BY ROLLUP(make,model);`