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 inTable 9.55 and statistical aggregates inTable 9.56. The built-in within-group ordered-set aggregate functions are listed inTable 9.57 while the built-in within-group hypothetical-set ones are inTable 9.58. Grouping operations, which are closely related to aggregate functions, are listed inTable 9.59. The special syntax considerations for aggregate functions are explained inSection 4.2.7. ConsultSection 2.7 for additional introductory information.
Table 9.55. 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, orinterval | 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 non-null input values |
bit_and( | smallint,int,bigint, orbit | same as argument data type | Yes | the bitwise AND of all non-null input values, or null if none |
bit_or( | smallint,int,bigint, orbit | 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 ofexpression is not null |
every( | bool | bool | Yes | equivalent tobool_and |
json_agg( | any | json | No | aggregates values, including nulls, as a JSON array |
jsonb_agg( | any | jsonb | No | aggregates values, including nulls, as a JSON array |
json_object_agg( | (any, any) | json | No | aggregates name/value pairs as a JSON object; values can be null, but not names |
jsonb_object_agg( | (any, any) | jsonb | No | aggregates name/value pairs as a JSON object; values can be null, but not names |
max( | any numeric, string, date/time, network, or enum type, or arrays of these types | same as argument type | Yes | maximum value ofexpression across all non-null input values |
min( | any numeric, string, date/time, network, or enum type, or arrays of these types | same as argument type | Yes | minimum value ofexpression across all non-null input values |
string_agg( | (text,text) or (bytea,bytea) | same as argument types | No | non-null input values concatenated into a string, separated by delimiter |
sum( | smallint,int,bigint,real,double precision,numeric,interval, ormoney | bigint forsmallint orint arguments,numeric forbigint arguments, otherwise the same as the argument data type | Yes | sum ofexpression across all non-null input values |
xmlagg( | xml | xml | No | concatenation of non-null XML values (see alsoSection 9.14.1.7) |
It should be noted that except forcount, 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, andarray_agg returns null rather than an empty array when there are no input rows. Thecoalesce function can be used to substitute zero or an empty array for null when necessary.
Aggregate functions which supportPartial Mode are eligible to participate in various optimizations, such as parallel aggregation.
Note
Boolean aggregatesbool_and andbool_or correspond to standard SQL aggregatesevery andany orsome. As forany andsome, it seems that there is an ambiguity built into the standard syntax:
SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;
HereANY 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 thecount 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 functionsarray_agg,json_agg,jsonb_agg,json_object_agg,jsonb_object_agg,string_agg, andxmlagg, 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 anORDER BY clause within the aggregate call, as shown inSection 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 9.56 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 mentionsN, 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 whenN is zero.
Table 9.56. Aggregate Functions for Statistics
Table 9.57 shows some aggregate functions that use theordered-set aggregate syntax. These functions are sometimes referred to as“inverse distribution” functions.
Table 9.57. Ordered-Set Aggregate Functions
All the aggregates listed inTable 9.57 ignore null values in their sorted input. For those that take afraction 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 inTable 9.58 is associated with a window function of the same name defined inSection 9.21. In each case, the aggregate result is the value that the associated window function would have returned for the“hypothetical” row constructed fromargs, if such a row had been added to the sorted group of rows computed from thesorted_args.
Table 9.58. Hypothetical-Set Aggregate Functions
For each of these hypothetical-set aggregates, the list of direct arguments given inargs must match the number and types of the aggregated arguments given insorted_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 theORDER BY clause.
Table 9.59. Grouping Operations
Grouping operations are used in conjunction with grouping sets (seeSection 7.2.4) to distinguish result rows. The arguments to theGROUPING operation are not actually evaluated, but they must match exactly expressions given in theGROUP 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:
=>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)