## 9.20. Aggregate Functions

*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.

### Note

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.

### 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 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

*is zero.*

`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

*. 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*

`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);`