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Aggregate functions compute a single result from a set of input values. The built-in general-purpose aggregate functions are listed in Table 9.55 and statistical aggregates in Table 9.56. The built-in within-group ordered-set aggregate functions are listed in Table 9.57 while the built-in within-group hypothetical-set ones are in Table 9.58. Grouping operations, which are closely related to aggregate functions, are listed in Table 9.59. The special syntax considerations for aggregate functions are explained in Section 4.2.7. Consult Section 2.7 for additional introductory information.
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:
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:
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:
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 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 9.57 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as “inverse distribution” functions.
All the aggregates listed in Table 9.57 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 9.58 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.
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.
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:
Function
Argument Type(s)
Return Type
Partial Mode
Description
array_agg(expression)
any non-array 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 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(expression)
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(expression)
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(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, including nulls, as a JSON array
jsonb_agg(expression)
any
jsonb
No
aggregates values, including nulls, as a JSON array
json_object_agg(name, value)
(any, any)
json
No
aggregates name/value pairs as a JSON object; values can be null, but not names
jsonb_object_agg(name, value)
(any, any)
jsonb
No
aggregates name/value pairs as a JSON object; values can be null, but not names
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 non-null 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 non-null input values
string_agg(expression, delimiter)
(text, text) or (bytea, bytea)
same as argument types
No
non-null 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 non-null input values
xmlagg(expression)
xml
xml
No
concatenation of non-null XML values (see also Section 9.14.1.7)
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
y-intercept of the least-squares-fit 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 least-squares-fit 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 floating-point arguments, otherwise numeric
Yes
historical alias for stddev_samp
stddev_pop(expression)
smallint, int, bigint, real, double precision, or numeric
double precision for floating-point 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 floating-point arguments, otherwise numeric
Yes
sample standard deviation of the input values
variance(expression)
smallint, int, bigint, real, double precision, or numeric
double precision for floating-point arguments, otherwise numeric
Yes
historical alias for var_samp
var_pop(expression)
smallint, int, bigint, real, double precision, or numeric
double precision for floating-point 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 floating-point arguments, otherwise numeric
Yes
sample variance of the input values (square of the sample standard deviation)
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 equally-frequent 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 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
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
Function
Return Type
Description
GROUPING(args...)
integer
Integer bit mask indicating which arguments are not being included in the current grouping set