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, orinterval | numericfor any integer-type argument,double precisionfor 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, 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 of expressionis not null | 
| 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 expressionacross all input
       values | 
| min( | any numeric, string, date/time, network, or enum type, or arrays of these types | same as argument type | Yes | minimum value of expressionacross all input
       values | 
|          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, ormoney | bigintforsmallintorintarguments,numericforbigintarguments, otherwise the same as the
       argument data type | Yes | sum of expressionacross all input values | 
| 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 is zero.
  
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.
   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 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:
=>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)