15.14. Aggregate Functions

Aggregate functions operate on a set of values to compute a single result.

Except for count(), count_if(), max_by(), min_by() and approx_distinct(), all of these aggregate functions ignore null values and return null for no input rows or when all values are null. For example, sum() returns null rather than zero and avg() does not include null values in the count. The coalesce function can be used to convert null into zero.

General Aggregate Functions

arbitrary(x) → [same as input]: Returns an arbitrary non-null value of x, if one exists.

array_agg(x) → array<[same as input]>: Returns an array created from the input x elements.

avg(x) → double: Returns the average (arithmetic mean) of all input values.

bool_and(boolean) → boolean: Returns TRUE if every input value is TRUE, otherwise FALSE.

bool_or(boolean) → boolean: Returns TRUE if any input value is TRUE, otherwise FALSE.

checksum(x) → varbinary: Returns an order-insensitive checksum of the given values.

count(*) → bigint: Returns the number of input rows.

count(x) → bigint: Returns the number of non-null input values.

count_if(x) → bigint: Returns the number of TRUE input values. This function is equivalent to count(CASE WHEN x THEN 1 END).

every(boolean) → boolean: This is an alias for bool_and().

geometric_mean(x) → double: Returns the geometric mean of all input values.

grouping(col1, ..., colN) → integer

Returns a bit set converted to decimal, indicating which columns are present in a grouping. The function must be used in conjunction with GROUPING SETS, ROLLUP or CUBE and its arguments must match exactly the columns referenced in the corresponding GROUPING SETS, ROLLUP or CUBE.

To compute the resulting bit set for a particular row, bits are assigned to the argument columns with the rightmost column being the least significant bit. For a given grouping, a bit is set to 0 if the corresponding column is included in the grouping and to 1 otherwise. For example, consider the query below:

SELECT origin_state, origin_zip, destination_state, sum(package_weight), grouping(origin_state, origin_zip, destination_state)
FROM shipping
GROUP BY GROUPING SETS (
        (origin_state),
        (origin_state, origin_zip),
        (destination_state));

origin_state | origin_zip | destination_state | _col3 | _col4
--------------+------------+-------------------+-------+-------
California   | NULL       | NULL              |  1397 |     3
New Jersey   | NULL       | NULL              |   225 |     3
New York     | NULL       | NULL              |     3 |     3
California   |      94131 | NULL              |    60 |     1
New Jersey   |       7081 | NULL              |   225 |     1
California   |      90210 | NULL              |  1337 |     1
New York     |      10002 | NULL              |     3 |     1
NULL         | NULL       | New Jersey        |    58 |     6
NULL         | NULL       | Connecticut       |  1562 |     6
NULL         | NULL       | Colorado          |     5 |     6
(10 rows)

The first grouping in the above result only includes the origin_state column and excludes the origin_zip and destination_state columns. The bit set constructed for that grouping is 011 where the most significant bit represents origin_state.

Map Aggregate Functions

histogram(x) → map<K,bigint>: Returns a map containing the count of the number of times each input value occurs.

map_agg(key, value) → map<K,V>: Returns a map created from the input key / value pairs.

map_union(x<K, V>) → map<K,V>: Returns the union of all the input maps. If a key is found in multiple input maps, that key’s value in the resulting map comes from an arbitrary input map.

multimap_agg(key, value) → map<K,array<V>>: Returns a multimap created from the input key / value pairs. Each key can be associated with multiple values.

Approximate Aggregate Functions

approx_distinct(x) → bigint

Returns the approximate number of distinct input values. This function provides an approximation of count(DISTINCT x). Zero is returned if all input values are null.

This function should produce a standard error of 2.3%, which is the standard deviation of the (approximately normal) error distribution over all possible sets. It does not guarantee an upper bound on the error for any specific input set.

approx_distinct(x, e) → bigint

Returns the approximate number of distinct input values. This function provides an approximation of count(DISTINCT x). Zero is returned if all input values are null.

This function should produce a standard error of no more than e, which is the standard deviation of the (approximately normal) error distribution over all possible sets. It does not guarantee an upper bound on the error for any specific input set. The current implementation of this function requires that e be in the range: [0.01150, 0.26000].

approx_percentile(x, percentage) → [same as x]: Returns the approximate percentile for all input values of x at the given percentage. The value of percentage must be between zero and one and must be constant for all input rows.

approx_percentile(x, percentages) → array<[same as x]>: Returns the approximate percentile for all input values of x at each of the specified percentages. Each element of the percentages array must be between zero and one, and the array must be constant for all input rows.

approx_percentile(x, w, percentage) → [same as x]: Returns the approximate weighed percentile for all input values of x using the per-item weight w at the percentage p. The weight must be an integer value of at least one. It is effectively a replication count for the value x in the percentile set. The value of p must be between zero and one and must be constant for all input rows.

approx_percentile(x, w, percentage, accuracy) → [same as x]: Returns the approximate weighed percentile for all input values of x using the per-item weight w at the percentage p, with a maximum rank error of accuracy. The weight must be an integer value of at least one. It is effectively a replication count for the value x in the percentile set. The value of p must be between zero and one and must be constant for all input rows. accuracy must be a value greater than zero and less than one, and it must be constant for all input rows.

approx_percentile(x, w, percentages) → array<[same as x]>: Returns the approximate weighed percentile for all input values of x using the per-item weight w at each of the given percentages specified in the array. The weight must be an integer value of at least one. It is effectively a replication count for the value x in the percentile set. Each element of the array must be between zero and one, and the array must be constant for all input rows.

numeric_histogram(buckets, value, weight) → map<double, double>

Computes an approximate histogram with up to buckets number of buckets for all values with a per-item weight of weight. The algorithm is based loosely on:

Yael Ben-Haim and Elad Tom-Tov, "A streaming parallel decision tree algorithm",
J. Machine Learning Research 11 (2010), pp. 849--872.

buckets must be a bigint. value and weight must be numeric.

numeric_histogram(buckets, value) → map<double, double>: Computes an approximate histogram with up to buckets number of buckets for all values. This function is equivalent to the variant of numeric_histogram() that takes a weight, with a per-item weight of 1.

Statistical Aggregate Functions

corr(y, x) → double: Returns correlation coefficient of input values.

covar_pop(y, x) → double: Returns the population covariance of input values.

covar_samp(y, x) → double: Returns the sample covariance of input values.

regr_intercept(y, x) → double: Returns linear regression intercept of input values. y is the dependent value. x is the independent value.

regr_slope(y, x) → double: Returns linear regression slope of input values. y is the dependent value. x is the independent value.

stddev(x) → double: This is an alias for stddev_samp().

stddev_pop(x) → double: Returns the population standard deviation of all input values.

stddev_samp(x) → double: Returns the sample standard deviation of all input values.

variance(x) → double: This is an alias for var_samp().

var_pop(x) → double: Returns the population variance of all input values.

var_samp(x) → double: Returns the sample variance of all input values.