Aggregate functions compute a single result from a set of input values. The builtin generalpurpose aggregate functions are listed in Table 9.52 and statistical aggregates in Table 9.53. The builtin withingroup orderedset aggregate functions are listed in Table 9.54 while the builtin withingroup hypotheticalset 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. GeneralPurpose Aggregate Functions
Function  Argument Type(s)  Return Type  Partial Mode  Description 

array_agg(expression)
 any nonarray type  array of the argument type  No  input values, including nulls, concatenated into an array 
array_agg(expression)  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(expression)

smallint , int , bigint , real , double precision , numeric , or interval

numeric for any integertype argument, double precision for a floatingpoint argument, otherwise the same as the argument data type  Yes  the average (arithmetic mean) of all input values 
bit_and(expression)

smallint , int , bigint , or bit
 same as argument data type  Yes  the bitwise AND of all nonnull input values, or null if none 
bit_or(expression)

smallint , int , bigint , or bit
 same as argument data type  Yes  the bitwise OR of all nonnull input values, or null if none 
bool_and(expression)
 bool  bool  Yes  true if all input values are true, otherwise false 
bool_or(expression)
 bool  bool  Yes  true if at least one input value is true, otherwise false 
count(*)
 bigint  Yes  number of input rows  
count(expression)  any  bigint  Yes  number of input rows for which the value of expression is not null 
every(expression)
 bool  bool  Yes  equivalent to bool_and

json_agg(expression)
 any  json  No  aggregates values as a JSON array 
jsonb_agg(expression)
 any  jsonb  No  aggregates values as a JSON array 
json_object_agg(name, value)
 (any, any)  json  No  aggregates name/value pairs as a JSON object 
jsonb_object_agg(name, value)
 (any, any)  jsonb  No  aggregates name/value pairs as a JSON object 
max(expression)
 any numeric, string, date/time, network, or enum type, or arrays of these types  same as argument type  Yes  maximum value of expression across all input values 
min(expression)
 any numeric, string, date/time, network, or enum type, or arrays of these types  same as argument type  Yes  minimum value of expression across all input values 
string_agg(expression, delimiter)
 (text , text ) or (bytea , bytea )  same as argument types  No  input values concatenated into a string, separated by delimiter 
sum(expression)

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 expression across all input values 
xmlagg(expression)
 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
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 ...;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 userdefined 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 morecommonlyused aggregates.) Where the description mentions N
, it means the number of input rows for which all the input expressions are nonnull. In all cases, null is returned if the computation is meaningless, for example when N
is zero.
Table 9.53. Aggregate Functions for Statistics
Function  Argument Type  Return Type  Partial Mode  Description 

corr(Y, X)
 double precision  double precision  Yes  correlation coefficient 
covar_pop(Y, X)
 double precision  double precision  Yes  population covariance 
covar_samp(Y, X)
 double precision  double precision  Yes  sample covariance 
regr_avgx(Y, X)
 double precision  double precision  Yes  average of the independent variable (sum(X)/N ) 
regr_avgy(Y, X)
 double precision  double precision  Yes  average of the dependent variable (sum(Y)/N ) 
regr_count(Y, X)
 double precision  bigint  Yes  number of input rows in which both expressions are nonnull 
regr_intercept(Y, X)
 double precision  double precision  Yes  yintercept of the leastsquaresfit linear equation determined by the (X , Y ) pairs 
regr_r2(Y, X)
 double precision  double precision  Yes  square of the correlation coefficient 
regr_slope(Y, X)
 double precision  double precision  Yes  slope of the leastsquaresfit linear equation determined by the (X , Y ) pairs 
regr_sxx(Y, X)
 double precision  double precision  Yes 
sum(X^2)  sum(X)^2/N (“sum of squares” of the independent variable) 
regr_sxy(Y, X)
 double precision  double precision  Yes 
sum(X*Y)  sum(X) * sum(Y)/N (“sum of products” of independent times dependent variable) 
regr_syy(Y, X)
 double precision  double precision  Yes 
sum(Y^2)  sum(Y)^2/N (“sum of squares” of the dependent variable) 
stddev(expression)

smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  historical alias for stddev_samp

stddev_pop(expression)

smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  population standard deviation of the input values 
stddev_samp(expression)

smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  sample standard deviation of the input values 
variance (expression ) 
smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  historical alias for var_samp

var_pop (expression ) 
smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  population variance of the input values (square of the population standard deviation) 
var_samp (expression ) 
smallint , int , bigint , real , double precision , or numeric

double precision for floatingpoint arguments, otherwise numeric
 Yes  sample variance of the input values (square of the sample standard deviation) 
Table 9.54 shows some aggregate functions that use the orderedset aggregate syntax. These functions are sometimes referred to as “inverse distribution” functions.
Table 9.54. OrderedSet Aggregate Functions
Function  Direct Argument Type(s)  Aggregated Argument Type(s)  Return Type  Partial Mode  Description 

mode() WITHIN GROUP (ORDER BY sort_expression)
 any sortable type  same as sort expression  No  returns the most frequent input value (arbitrarily choosing the first one if there are multiple equallyfrequent results)  
percentile_cont(fraction) WITHIN GROUP (ORDER BY sort_expression)
 double precision 
double precision or interval
 same as sort expression  No  continuous percentile: returns a value corresponding to the specified fraction in the ordering, interpolating between adjacent input items if needed 
percentile_cont(fractions) WITHIN GROUP (ORDER BY sort_expression)  double precision[] 
double precision or interval
 array of sort expression's type  No  multiple continuous percentile: returns an array of results matching the shape of the fractions parameter, with each nonnull element replaced by the value corresponding to that percentile 
percentile_disc(fraction) WITHIN GROUP (ORDER BY sort_expression)
 double precision  any sortable type  same as sort expression  No  discrete percentile: returns the first input value whose position in the ordering equals or exceeds the specified fraction 
percentile_disc(fractions) WITHIN GROUP (ORDER BY sort_expression)  double precision[]  any sortable type  array of sort expression's type  No  multiple discrete percentile: returns an array of results matching the shape of the fractions parameter, with each nonnull element replaced by the input value corresponding to that percentile 
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. HypotheticalSet Aggregate Functions
Function  Direct Argument Type(s)  Aggregated Argument Type(s)  Return Type  Partial Mode  Description 

rank(args) WITHIN GROUP (ORDER BY sorted_args)

VARIADIC "any"

VARIADIC "any"
 bigint  No  rank of the hypothetical row, with gaps for duplicate rows 
dense_rank(args) WITHIN GROUP (ORDER BY sorted_args)

VARIADIC "any"

VARIADIC "any"
 bigint  No  rank of the hypothetical row, without gaps 
percent_rank(args) WITHIN GROUP (ORDER BY sorted_args)

VARIADIC "any"

VARIADIC "any"
 double precision  No  relative rank of the hypothetical row, ranging from 0 to 1 
cume_dist(args) WITHIN GROUP (ORDER BY sorted_args)

VARIADIC "any"

VARIADIC "any"
 double precision  No  relative rank of the hypothetical row, ranging from 1/N to 1 
For each of these hypotheticalset 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 builtin 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
Function  Return Type  Description 

GROUPING(args...)
 integer  Integer bit mask indicating which arguments are not being included in the current grouping set 
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 leastsignificant 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)
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