9.20. 彙總函數

https://www.postgresql.org/docs/12/functions-aggregate.html

Table 9.55 中列出了內建的通用型彙總函數,Table 9.56 中列出了統計類的彙總函數。Table 9.57 中列出了內建的有序集合函數。群組內部假設集合則在 Table 9.58 之中。與資料表彙總函數密切相關的組群操作列於 Table 9.59 中。彙總函數的特殊語法注意事項在 4.2.7 節中說明。有關其他介紹性資訊,請參閱第 2.7 節

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 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 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)

應該注意的是,除了 count 之外,這些函數在沒有資料列被篩選到時回傳 null。特別是,空資料列的總和計算會回傳 null,而不是如人們所期望的那樣為零,並且當沒有輸入資料列時,array_agg 回傳 null 而不是空陣列。必要時,coalesce 函數可用於將零或空陣列替換為 null。

支持 Parital Mode 的彙總函數有資格參與各種最佳化,例如平行彙總計算。

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 列出了一些使用有序集合彙總語法的彙總函數。這些函數有時被稱為“逆分佈”函數。

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)