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 inTable 9.60 while statistical aggregates are inTable 9.61. The built-in within-group ordered-set aggregate functions are listed inTable 9.62 while the built-in within-group hypothetical-set ones are inTable 9.63. Grouping operations, which are closely related to aggregate functions, are listed inTable 9.64. The special syntax considerations for aggregate functions are explained inSection 4.2.7. ConsultSection 2.7 for additional introductory information.
Aggregate functions that supportPartial Mode are eligible to participate in various optimizations, such as parallel aggregation.
While all aggregates below accept an optionalORDER BY clause (as outlined inSection 4.2.7), the clause has only been added to aggregates whose output is affected by ordering.
Table 9.60. General-Purpose Aggregate Functions
Function Description | Partial Mode |
|---|
any_value (anyelement ) →same as input type
Returns an arbitrary value from the non-null input values. | Yes |
array_agg (anynonarrayORDER BYinput_sort_columns ) →anyarray
Collects all the input values, including nulls, into an array. | Yes |
array_agg (anyarrayORDER BYinput_sort_columns ) →anyarray
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.) | Yes |
avg (smallint ) →numeric
avg (integer ) →numeric
avg (bigint ) →numeric
avg (numeric ) →numeric
avg (real ) →double precision
avg (double precision ) →double precision
avg (interval ) →interval
Computes the average (arithmetic mean) of all the non-null input values. | Yes |
bit_and (smallint ) →smallint
bit_and (integer ) →integer
bit_and (bigint ) →bigint
bit_and (bit ) →bit
Computes the bitwise AND of all non-null input values. | Yes |
bit_or (smallint ) →smallint
bit_or (integer ) →integer
bit_or (bigint ) →bigint
bit_or (bit ) →bit
Computes the bitwise OR of all non-null input values. | Yes |
bit_xor (smallint ) →smallint
bit_xor (integer ) →integer
bit_xor (bigint ) →bigint
bit_xor (bit ) →bit
Computes the bitwise exclusive OR of all non-null input values. Can be useful as a checksum for an unordered set of values. | Yes |
bool_and (boolean ) →boolean
Returns true if all non-null input values are true, otherwise false. | Yes |
bool_or (boolean ) →boolean
Returns true if any non-null input value is true, otherwise false. | Yes |
count (* ) →bigint
Computes the number of input rows. | Yes |
count ("any" ) →bigint
Computes the number of input rows in which the input value is not null. | Yes |
every (boolean ) →boolean
This is the SQL standard's equivalent tobool_and. | Yes |
json_agg (anyelementORDER BYinput_sort_columns ) →json
jsonb_agg (anyelementORDER BYinput_sort_columns ) →jsonb
Collects all the input values, including nulls, into a JSON array. Values are converted to JSON as perto_json orto_jsonb. | No |
json_agg_strict (anyelement ) →json
jsonb_agg_strict (anyelement ) →jsonb
Collects all the input values, skipping nulls, into a JSON array. Values are converted to JSON as perto_json orto_jsonb. | No |
json_arrayagg ( [value_expression] [ORDER BYsort_expression] [ {NULL |ABSENT }ON NULL] [RETURNINGdata_type [FORMAT JSON [ENCODING UTF8]]])
Behaves in the same way asjson_array but as an aggregate function so it only takes onevalue_expression parameter. IfABSENT ON NULL is specified, any NULL values are omitted. IfORDER BY is specified, the elements will appear in the array in that order rather than in the input order. SELECT json_arrayagg(v) FROM (VALUES(2),(1)) t(v) →[2, 1]
| No |
json_objectagg ( [ {key_expression {VALUE | ':' }value_expression }] [ {NULL |ABSENT }ON NULL] [ {WITH |WITHOUT }UNIQUE [KEYS]] [RETURNINGdata_type [FORMAT JSON [ENCODING UTF8]]])
Behaves likejson_object, but as an aggregate function, so it only takes onekey_expression and onevalue_expression parameter. SELECT json_objectagg(k:v) FROM (VALUES ('a'::text,current_date),('b',current_date + 1)) AS t(k,v) →{ "a" : "2022-05-10", "b" : "2022-05-11" }
| No |
json_object_agg (key"any",value"any"ORDER BYinput_sort_columns ) →json
jsonb_object_agg (key"any",value"any"ORDER BYinput_sort_columns ) →jsonb
Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as perto_json orto_jsonb. Values can be null, but keys cannot. | No |
json_object_agg_strict (key"any",value"any" ) →json
jsonb_object_agg_strict (key"any",value"any" ) →jsonb
Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as perto_json orto_jsonb. Thekey can not be null. If thevalue is null then the entry is skipped, | No |
json_object_agg_unique (key"any",value"any" ) →json
jsonb_object_agg_unique (key"any",value"any" ) →jsonb
Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as perto_json orto_jsonb. Values can be null, but keys cannot. If there is a duplicate key an error is thrown. | No |
json_object_agg_unique_strict (key"any",value"any" ) →json
jsonb_object_agg_unique_strict (key"any",value"any" ) →jsonb
Collects all the key/value pairs into a JSON object. Key arguments are coerced to text; value arguments are converted as perto_json orto_jsonb. Thekey can not be null. If thevalue is null then the entry is skipped. If there is a duplicate key an error is thrown. | No |
max (see text ) →same as input type
Computes the maximum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well asinet,interval,money,oid,pg_lsn,tid,xid8, and arrays of any of these types. | Yes |
min (see text ) →same as input type
Computes the minimum of the non-null input values. Available for any numeric, string, date/time, or enum type, as well asinet,interval,money,oid,pg_lsn,tid,xid8, and arrays of any of these types. | Yes |
range_agg (valueanyrange ) →anymultirange
range_agg (valueanymultirange ) →anymultirange
Computes the union of the non-null input values. | No |
range_intersect_agg (valueanyrange ) →anyrange
range_intersect_agg (valueanymultirange ) →anymultirange
Computes the intersection of the non-null input values. | No |
string_agg (valuetext,delimitertext ) →text
string_agg (valuebytea,delimiterbyteaORDER BYinput_sort_columns ) →bytea
Concatenates the non-null input values into a string. Each value after the first is preceded by the correspondingdelimiter (if it's not null). | Yes |
sum (smallint ) →bigint
sum (integer ) →bigint
sum (bigint ) →numeric
sum (numeric ) →numeric
sum (real ) →real
sum (double precision ) →double precision
sum (interval ) →interval
sum (money ) →money
Computes the sum of the non-null input values. | Yes |
xmlagg (xmlORDER BYinput_sort_columns ) →xml
Concatenates the non-null XML input values (seeSection 9.15.1.8). | No |
It should be noted that except forcount, 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, andarray_agg returns null rather than an empty array when there are no input rows. Thecoalesce function can be used to substitute zero or an empty array for null when necessary.
The aggregate functionsarray_agg,json_agg,jsonb_agg,json_agg_strict,jsonb_agg_strict,json_object_agg,jsonb_object_agg,json_object_agg_strict,jsonb_object_agg_strict,json_object_agg_unique,jsonb_object_agg_unique,json_object_agg_unique_strict,jsonb_object_agg_unique_strict,string_agg, andxmlagg, 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 anORDER BY clause within the aggregate call, as shown inSection 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.
Note
The boolean aggregatesbool_and andbool_or correspond to the standard SQL aggregatesevery andany orsome.PostgreSQL supportsevery, but notany orsome, because there is an ambiguity built into the standard syntax:
SELECT b1 = ANY((SELECT b2 FROM t2 ...)) FROM t1 ...;
HereANY 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 thecount 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 9.61 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 acceptingnumeric_type are available for all the typessmallint,integer,bigint,numeric,real, anddouble precision. Where the description mentionsN, 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 whenN is zero.
Table 9.61. Aggregate Functions for Statistics
Function Description | Partial Mode |
|---|
corr (Ydouble precision,Xdouble precision ) →double precision
Computes the correlation coefficient. | Yes |
covar_pop (Ydouble precision,Xdouble precision ) →double precision
Computes the population covariance. | Yes |
covar_samp (Ydouble precision,Xdouble precision ) →double precision
Computes the sample covariance. | Yes |
regr_avgx (Ydouble precision,Xdouble precision ) →double precision
Computes the average of the independent variable,sum(X)/N. | Yes |
regr_avgy (Ydouble precision,Xdouble precision ) →double precision
Computes the average of the dependent variable,sum(Y)/N. | Yes |
regr_count (Ydouble precision,Xdouble precision ) →bigint
Computes the number of rows in which both inputs are non-null. | Yes |
regr_intercept (Ydouble precision,Xdouble precision ) →double precision
Computes the y-intercept of the least-squares-fit linear equation determined by the (X,Y) pairs. | Yes |
regr_r2 (Ydouble precision,Xdouble precision ) →double precision
Computes the square of the correlation coefficient. | Yes |
regr_slope (Ydouble precision,Xdouble precision ) →double precision
Computes the slope of the least-squares-fit linear equation determined by the (X,Y) pairs. | Yes |
regr_sxx (Ydouble precision,Xdouble precision ) →double precision
Computes the“sum of squares” of the independent variable,sum(X^2) - sum(X)^2/N. | Yes |
regr_sxy (Ydouble precision,Xdouble precision ) →double precision
Computes the“sum of products” of independent times dependent variables,sum(X*Y) - sum(X) * sum(Y)/N. | Yes |
regr_syy (Ydouble precision,Xdouble precision ) →double precision
Computes the“sum of squares” of the dependent variable,sum(Y^2) - sum(Y)^2/N. | Yes |
stddev (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
This is a historical alias forstddev_samp. | Yes |
stddev_pop (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
Computes the population standard deviation of the input values. | Yes |
stddev_samp (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
Computes the sample standard deviation of the input values. | Yes |
variance (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
This is a historical alias forvar_samp. | Yes |
var_pop (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
Computes the population variance of the input values (square of the population standard deviation). | Yes |
var_samp (numeric_type ) →double precision forreal ordouble precision, otherwisenumeric
Computes the sample variance of the input values (square of the sample standard deviation). | Yes |
Table 9.62 shows some aggregate functions that use theordered-set aggregate syntax. These functions are sometimes referred to as“inverse distribution” functions. Their aggregated input is introduced byORDER BY, and they may also take adirect argument that is not aggregated, but is computed only once. All these functions ignore null values in their aggregated input. For those that take afraction parameter, the fraction value must be between 0 and 1; an error is thrown if not. However, a nullfraction value simply produces a null result.
Table 9.62. Ordered-Set Aggregate Functions
Function Description | Partial Mode |
|---|
mode ()WITHIN GROUP (ORDER BYanyelement ) →anyelement
Computes themode, the most frequent value of the aggregated argument (arbitrarily choosing the first one if there are multiple equally-frequent values). The aggregated argument must be of a sortable type. | No |
percentile_cont (fractiondouble precision )WITHIN GROUP (ORDER BYdouble precision ) →double precision
percentile_cont (fractiondouble precision )WITHIN GROUP (ORDER BYinterval ) →interval
Computes thecontinuous percentile, a value corresponding to the specifiedfraction within the ordered set of aggregated argument values. This will interpolate between adjacent input items if needed. | No |
percentile_cont (fractionsdouble precision[] )WITHIN GROUP (ORDER BYdouble precision ) →double precision[]
percentile_cont (fractionsdouble precision[] )WITHIN GROUP (ORDER BYinterval ) →interval[]
Computes multiple continuous percentiles. The result is an array of the same dimensions as thefractions parameter, with each non-null element replaced by the (possibly interpolated) value corresponding to that percentile. | No |
percentile_disc (fractiondouble precision )WITHIN GROUP (ORDER BYanyelement ) →anyelement
Computes thediscrete percentile, the first value within the ordered set of aggregated argument values whose position in the ordering equals or exceeds the specifiedfraction. The aggregated argument must be of a sortable type. | No |
percentile_disc (fractionsdouble precision[] )WITHIN GROUP (ORDER BYanyelement ) →anyarray
Computes multiple discrete percentiles. The result is an array of the same dimensions as thefractions parameter, with each non-null element replaced by the input value corresponding to that percentile. The aggregated argument must be of a sortable type. | No |
Each of the“hypothetical-set” aggregates listed inTable 9.63 is associated with a window function of the same name defined inSection 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 fromargs, if such a row had been added to the sorted group of rows represented by thesorted_args. For each of these functions, the list of direct arguments given inargs must match the number and types of the aggregated arguments given insorted_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 theORDER BY clause.
Table 9.63. Hypothetical-Set Aggregate Functions
Function Description | Partial Mode |
|---|
rank (args )WITHIN GROUP (ORDER BYsorted_args ) →bigint
Computes the rank of the hypothetical row, with gaps; that is, the row number of the first row in its peer group. | No |
dense_rank (args )WITHIN GROUP (ORDER BYsorted_args ) →bigint
Computes the rank of the hypothetical row, without gaps; this function effectively counts peer groups. | No |
percent_rank (args )WITHIN GROUP (ORDER BYsorted_args ) →double precision
Computes the relative rank of the hypothetical row, that is (rank - 1) / (total rows - 1). The value thus ranges from 0 to 1 inclusive. | No |
cume_dist (args )WITHIN GROUP (ORDER BYsorted_args ) →double precision
Computes the cumulative distribution, that is (number of rows preceding or peers with hypothetical row) / (total rows). The value thus ranges from 1/N to 1. | No |
Table 9.64. Grouping Operations
Function Description |
|---|
GROUPING (group_by_expression(s) ) →integer
Returns a bit mask indicating whichGROUP BY expressions are not included in the current grouping set. Bits are assigned with the rightmost argument corresponding to the least-significant bit; each bit is 0 if the corresponding expression is included in the grouping criteria of the grouping set generating the current result row, and 1 if it is not included. |
The grouping operations shown inTable 9.64 are used in conjunction with grouping sets (seeSection 7.2.4) to distinguish result rows. The arguments to theGROUPING function are not actually evaluated, but they must exactly match expressions given in theGROUP 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)
Here, thegrouping value0 in the first four rows shows that those have been grouped normally, over both the grouping columns. The value1 indicates thatmodel was not grouped by in the next-to-last two rows, and the value3 indicates that neithermake normodel was grouped by in the last row (which therefore is an aggregate over all the input rows).
