9.20. 彙總函數

版本：11

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.

Table 9.55. General-Purpose Aggregate Functions

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)

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: 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 9.56 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 9.56. 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	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)

Table 9.57 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as “inverse distribution” functions.

Table 9.57. Ordered-Set 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 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

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.

Table 9.58. Hypothetical-Set 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 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 9.59. 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 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)