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

**Table 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 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.

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 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.

Table 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`

**Table 9.53. Aggregate
Functions for Statistics**

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

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

All the aggregates listed in Table 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 Table 9.55
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 9.55. 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.**Table 9.56. Grouping
Operations**

Grouping operations are used in conjunction with grouping sets
(see Section 7.2.4)
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);`

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