14th September 2023:
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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 9.55 while statistical aggregates are in Table 9.56. The built-in within-group ordered-set aggregate functions are listed in Table 9.57 while the built-in within-group hypothetical-set ones are in Table 9.58. Grouping operations, which are closely related to aggregate functions, are listed in Table 9.59. 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 9.55. 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 |

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 |

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 9.56 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`

`N`

**Table 9.56. Aggregate Functions for Statistics**

Table 9.57 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`

**Table 9.57. Ordered-Set Aggregate Functions**

Each of the “hypothetical-set” aggregates listed in Table 9.58 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`

`args`

`sorted_args`

`ORDER BY`

clause.**Table 9.58. Hypothetical-Set Aggregate Functions**

**Table 9.59. Grouping Operations**

The grouping operations shown in Table 9.59 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:

`=>`

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);`

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).

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