9.20. Aggregate Functions

Aggregate functions compute a single result from a set of input values. The built-in normal aggregate functions are listed in Table 9.51 and Table 9.52. The built-in ordered-set aggregate functions are listed in Table 9.53 and Table 9.54. Grouping operations, which are closely related to aggregate functions, are listed in Table 9.55. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.

Table 9.51. General-Purpose Aggregate Functions

FunctionArgument Type(s)Return TypePartial ModeDescription
array_agg(expression) any non-array type array of the argument type Noinput values, including nulls, concatenated into an array
array_agg(expression) any array type same as argument data type Noinput 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 intervalnumeric for any integer-type argument, double precision for a floating-point argument, otherwise the same as the argument data type Yesthe average (arithmetic mean) of all input values
bit_and(expression)smallint, int, bigint, or bit same as argument data type Yesthe bitwise AND of all non-null input values, or null if none
bit_or(expression)smallint, int, bigint, or bit same as argument data type Yesthe bitwise OR of all non-null input values, or null if none
bool_and(expression)boolboolYestrue if all input values are true, otherwise false
bool_or(expression)boolboolYestrue if at least one input value is true, otherwise false
count(*) bigintYesnumber of input rows
count(expression)anybigintYes number of input rows for which the value of expression is not null
every(expression)boolboolYesequivalent to bool_and
json_agg(expression)anyjsonNoaggregates values as a JSON array
jsonb_agg(expression)anyjsonbNoaggregates values as a JSON array
json_object_agg(name, value)(any, any)jsonNoaggregates name/value pairs as a JSON object
jsonb_object_agg(name, value)(any, any)jsonbNoaggregates name/value pairs as a JSON object
max(expression)any numeric, string, date/time, network, or enum type, or arrays of these typessame as argument typeYes maximum value of expression across all input values
min(expression)any numeric, string, date/time, network, or enum type, or arrays of these typessame as argument typeYes minimum value of expression across all input values
string_agg(expression, delimiter) (text, text) or (bytea, bytea) same as argument types Noinput values concatenated into a string, separated by delimiter
sum(expression)smallint, int, bigint, real, double precision, numeric, interval, or moneybigint for smallint or int arguments, numeric for bigint arguments, otherwise the same as the argument data type Yessum of expression across all input values
xmlagg(expression)xmlxmlNoconcatenation 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: Postgres Pro 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 9.52 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 9.52. Aggregate Functions for Statistics

FunctionArgument TypeReturn TypePartial ModeDescription
corr(Y, X)double precisiondouble precisionYescorrelation coefficient
covar_pop(Y, X)double precisiondouble precisionYespopulation covariance
covar_samp(Y, X)double precisiondouble precisionYessample covariance
regr_avgx(Y, X)double precisiondouble precisionYesaverage of the independent variable (sum(X)/N)
regr_avgy(Y, X)double precisiondouble precisionYesaverage of the dependent variable (sum(Y)/N)
regr_count(Y, X)double precisionbigintYesnumber of input rows in which both expressions are nonnull
regr_intercept(Y, X)double precisiondouble precisionYesy-intercept of the least-squares-fit linear equation determined by the (X, Y) pairs
regr_r2(Y, X)double precisiondouble precisionYessquare of the correlation coefficient
regr_slope(Y, X)double precisiondouble precisionYesslope of the least-squares-fit linear equation determined by the (X, Y) pairs
regr_sxx(Y, X)double precisiondouble precisionYessum(X^2) - sum(X)^2/N (sum of squares of the independent variable)
regr_sxy(Y, X)double precisiondouble precisionYessum(X*Y) - sum(X) * sum(Y)/N (sum of products of independent times dependent variable)
regr_syy(Y, X)double precisiondouble precisionYessum(Y^2) - sum(Y)^2/N (sum of squares of the dependent variable)
stddev(expression)smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYeshistorical alias for stddev_samp
stddev_pop(expression)smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYespopulation standard deviation of the input values
stddev_samp(expression)smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYessample standard deviation of the input values
variance(expression) smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYeshistorical alias for var_samp
var_pop(expression) smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYespopulation variance of the input values (square of the population standard deviation)
var_samp(expression) smallint, int, bigint, real, double precision, or numericdouble precision for floating-point arguments, otherwise numericYessample variance of the input values (square of the sample standard deviation)

Table 9.53 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as inverse distribution functions.

Table 9.53. Ordered-Set Aggregate Functions

FunctionDirect Argument Type(s)Aggregated Argument Type(s)Return TypePartial ModeDescription
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 equally-frequent results)
percentile_cont(fraction) WITHIN GROUP (ORDER BY sort_expression)double precisiondouble 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 non-null 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 non-null element replaced by the input value corresponding to that percentile

All the aggregates listed in Table 9.53 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 9.54 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 9.54. Hypothetical-Set Aggregate Functions

FunctionDirect Argument Type(s)Aggregated Argument Type(s)Return TypePartial ModeDescription
rank(args) WITHIN GROUP (ORDER BY sorted_args)VARIADIC "any"VARIADIC "any"bigintNo rank of the hypothetical row, with gaps for duplicate rows
dense_rank(args) WITHIN GROUP (ORDER BY sorted_args)VARIADIC "any"VARIADIC "any"bigintNo rank of the hypothetical row, without gaps
percent_rank(args) WITHIN GROUP (ORDER BY sorted_args)VARIADIC "any"VARIADIC "any"double precisionNo 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 precisionNo relative rank of the hypothetical row, ranging from 1/N to 1

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 9.55. Grouping Operations

FunctionReturn TypeDescription
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 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)