9.21. Aggregate Functions
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 while statistical aggregates are 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.
Aggregate functions that support Partial Mode are eligible to participate in various optimizations, such as parallel aggregation.
Table 9.55. General-Purpose Aggregate Functions
Collects all the input values, including nulls, into an array.
Concatenates all the input arrays into an array of one higher dimension. (The inputs must all have the same dimensionality, and cannot be empty or null.)
Computes the average (arithmetic mean) of all the non-null input values.
Computes the bitwise AND of all non-null input values.
Computes the bitwise OR of all non-null input values.
Returns true if all non-null input values are true, otherwise false.
Returns true if any non-null input value is true, otherwise false.
Computes the number of input rows.
Computes the number of input rows in which the input value is not null.
This is the SQL standard's equivalent to
Collects all the input values, including nulls, into a JSON array. Values are converted to JSON as per
Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as per
Computes the maximum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as
Computes the minimum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well as
Concatenates the non-null input values into a string. Each value after the first is preceded by the corresponding
Computes the sum of the non-null input values.
Concatenates the non-null XML input values (see Section 18.104.22.168).
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.
The aggregate functions
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.
The boolean aggregates
bool_or correspond to the standard SQL aggregates
some. PostgreSQL supports
every, but not
some, because there is an ambiguity built into the standard syntax:
SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;
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:
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 that includes all rows in the table.
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.) Functions shown as accepting
numeric_type are available for all the types
double precision. 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
Table 9.57 shows some aggregate functions that use the ordered-set aggregate syntax. These functions are sometimes referred to as “inverse distribution” functions. Their aggregated input is introduced by
ORDER BY, and they may also take a direct argument that is not aggregated, but is computed only once. All these functions ignore null values in their aggregated 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.
Table 9.57. Ordered-Set Aggregate Functions
Each of the “hypothetical-set” aggregates listed in Table 9.58 is associated with a window function of the same name defined in Section 9.22. In each case, the aggregate's 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 represented by the
sorted_args. For each of these functions, 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 9.58. Hypothetical-Set Aggregate Functions
Table 9.59. Grouping Operations
The grouping operations shown in Table 9.59 are used in conjunction with grouping sets (see Section 7.2.4) to distinguish result rows. The arguments to the
GROUPING function are not actually evaluated, but they must exactly match expressions given in the
GROUP BY clause of the associated query level. 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)
0 in the first four rows shows that those have been grouped normally, over both the grouping columns. The value
1 indicates that
model was not grouped by in the next-to-last two rows, and the value
3 indicates that neither
model was grouped by in the last row (which therefore is an aggregate over all the input rows).