For details about the supported syntaxes of aggregate expressions, see Aggregate function calls.

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General-purpose aggregate functions

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approx_count_distinct

Added in v2.5.0.
Counts the estimated number of distinct values in a column, using the HyperLogLog algorithm.
This function is only supported for append-only streams, and it is currently in technical preview stage.
approx_count_distinct ( expression ) -> bigint

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array_agg

Collects all the input values, including nulls, into an array. The ORDER BY clause is optional and specifies the order of rows processed in the aggregation, which determines the order of the elements in the result array. 
array_agg ( expression [ ORDER BY sort_expression ] ) -> output_array

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avg

Returns the average (arithmetic mean) of all non-null input values or null if no non-null values are provided. 
avg ( expression ) -> see description
Input types include smallint, int, bigint, numeric, real, and double precision. Return type is numeric for integer inputs and double precision for float point inputs.

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bit_and

Returns the bitwise AND of all non-null input values or null if no non-null values are provided. 
bit_and ( smallint | int | bigint ) -> same as input type

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bit_or

Returns the bitwise OR of all non-null input values or null if no non-null values are provided. 
bit_or ( smallint | int | bigint ) -> same as input type

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bool_and

Returns true if all non-null input values are true, otherwise false. 
bool_and ( boolean ) -> boolean

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bool_or

Returns true if any non-null input value is true, otherwise false. 
bool_or ( boolean ) -> boolean

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count

Returns the number of non-null input values. 
count ( expression ) -> bigint
The input can be of any supported data type.

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count(*)

Returns the number of rows in the input. 
count(*) -> bigint

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jsonb_agg

Collects all the input values, including nulls, into a JSON array. The ORDER BY clause is optional and specifies the order of rows processed in the aggregation, which determines the order of the elements in the result array. 
jsonb_agg ( any_element [ ORDER BY sort_expression ] ) -> jsonb

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jsonb_object_agg

Aggregates name/value pairs as a JSON object. Values can be null, but keys cannot. 
jsonb_object_agg ( key "text" , value "any" ) -> jsonb

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max

Returns the maximum of the non-null input values, or null if no non-null values are provided. 
max ( expression ) -> same as input type
Input can be of any numeric, string, date/time, or interval type, or an array of these types.

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min

Returns the minimum value of the non-null input values, or null if no non-null values are provided. 
min ( expression ) -> same as input type
Input can be of any numeric, string, date/time, or interval type, or an array of these types.

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string_agg

Concatenates non-null input values into a string. Each value after the first is preceded by the corresponding delimiter (if it’s not null). If no non-null values are provided, returns null. The ORDER BY clause is optional and specifies the order of rows processed in the aggregation, which determines the order of the elements in the result array. 
string_agg ( value text, delimiter text [ ORDER BY sort_expression ] ) -> output_string

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sum

Returns the sum of all non-null input values, or null if no non-null values are provided. 
sum ( expression )
Input types include smallint, int, bigint, numeric, real, and double precision. Return type is bigint for smallint or int inputs, numeric for bigint inputs, otherwise the same as the input data type.

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first_value

Returns the first value in an ordered set of values, including nulls. 
first_value ( expression ORDER BY order_key ) -> same as input type
order_key is the column or expression used to determine the order of the values. It is required to make the result deterministic.

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last_value

Returns the last value in an ordered set of values, including nulls. 
last_value ( expression ORDER BY order_key ) -> same as input type

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Aggregate functions for statistics

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stddev_pop

Calculates the population standard deviation of the input values. Returns NULL if the input contains no non-null values. 
stddev_pop ( expression ) -> output_value

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stddev_samp

Calculates the sample standard deviation of the input values. Returns NULL if the input contains fewer than two non-null values. 
stddev_samp ( expression ) -> output_value

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var_pop

Calculates the population variance of the input values. Returns NULL if the input contains no non-null values. 
var_pop ( expression ) -> output_value

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var_samp

Calculates the sample variance of the input values. Returns NULL if the input contains fewer than two non-null values. 
var_samp ( expression ) -> output_value

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Ordered-set aggregate functions

At present, ordered-set aggregate functions support only constant fraction arguments.

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mode

Computes the mode, which is the most frequent value of the aggregated argument. If there are multiple equally-frequent values, it arbitrarily chooses the first one. 
mode () WITHIN GROUP ( ORDER BY sort_expression ) -> same as sort_expression
sort_expression: Must be of a sortable type. This example calculates the mode of the values in column1 from table1. 
SELECT mode() WITHIN GROUP (ORDER BY column1) FROM table1;

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percentile_cont

At present, percentile_cont is not supported for streaming queries yet.
Computes the continuous percentile, which is a value corresponding to the specified fraction within the ordered set of aggregated argument values. It can interpolate between adjacent input items if needed. 
percentile_cont ( fraction double precision ) WITHIN GROUP ( ORDER BY sort_expression double precision ) -> double precision
fraction: The fraction value representing the desired percentile. It should be between 0 and 1. This example calculates the median (50th percentile) of the values in column1 from table1. 
SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY column1) FROM table1;
If NULL is provided, the function will not calculate a specific percentile and return NULL instead.

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percentile_disc

At present, percentile_disc is not supported for streaming queries yet.
Computes the discrete percentile, which is the first value within the ordered set of aggregated argument values whose position in the ordering equals or exceeds the specified fraction. 
percentile_disc ( fraction double precision ) WITHIN GROUP ( ORDER BY sort_expression anyelement ) -> same as sort_expression
fraction: The fraction value representing the desired percentile. It should be between 0 and 1. sort_expression: Must be of a sortable type. This example calculates the 75th percentile of the values in column1 from table1. 
SELECT percentile_disc(0.75) WITHIN GROUP (ORDER BY column1) FROM table1;
If NULL is provided, the function will not calculate a specific percentile and return NULL instead.

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approx_percentile

Returns an approximate value of the specified percentile from a numeric column. 
approx_percentile(DOUBLE percentile [, DOUBLE relative_error]) WITHIN GROUP (ORDER BY percentile_column) -> output_value
  • percentile: The percentile to approximate. It should be between 0 and 1.
  • relative_error: Optional. Specifies the maximum allowed error in the approximation. Defaults to 0.01 (1%).
  • percentile_column: The column from which to calculate the percentile. Must be of a numeric type.
This example calculates the 50th percentile of a numeric column with the default relative error: 
SELECT approx_percentile(0.5) WITHIN GROUP (ORDER BY column1) FROM table1;

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Grouping operation functions

Grouping operation functions are used in conjunction with grouping sets 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.

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grouping

Returns a bit mask indicating which GROUP 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.
Syntax
grouping ( group_by_expression(s) ) → integer

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Example

Create a table
CREATE TABLE items_sold (brand varchar, size varchar, sales int);
Insert some data
INSERT INTO items_sold VALUES ('Foo', 'L', 10),('Foo', 'M', 20),('Bar', 'M', 15),('Bar', 'L', '5');
Get grouping results
SELECT brand, size, sum(sales), grouping(brand), grouping(size), grouping(brand,size), count(DISTINCT sales)
FROM items_sold
GROUP BY GROUPING SETS ((brand), (size), ());
------RESULTS
Bar NULL 20 0 1 1 2
Foo NULL 30 0 1 1 2
NULL L 15 1 0 2 2
NULL M 35 1 0 2 2
NULL NULL 50 1 1 3 4