# 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.

Some aggregate functions such as array_agg() produce different results depending on the order of input values. This ordering can be specified by writing an ORDER BY Clause within the aggregate function:

array_agg(x ORDER BY y DESC)
array_agg(x ORDER BY x, y, z)


## 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.

avg(time interval type) → time interval type#

Returns the average interval length 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.

max_by(x, y) → [same as x]#

Returns the value of x associated with the maximum value of y over all input values.

max_by(x, y, n) → array<[same as x]>#

Returns n values of x associated with the n largest of all input values of y in descending order of y.

min_by(x, y) → [same as x]#

Returns the value of x associated with the minimum value of y over all input values.

min_by(x, y, n) → array<[same as x]>#

Returns n values of x associated with the n smallest of all input values of y in ascending order of y.

max(x) → [same as input]#

Returns the maximum value of all input values.

max(x, n) → array<[same as x]>#

Returns n largest values of all input values of x.

min(x) → [same as input]#

Returns the minimum value of all input values.

min(x, n) → array<[same as x]>#

Returns n smallest values of all input values of x.

reduce_agg(inputValue T, initialState S, inputFunction(S, T, S), combineFunction(S, S, S)) → S#

Reduces all input values into a single value. inputFunction will be invoked for each input value. In addition to taking the input value, inputFunction takes the current state, initially initialState, and returns the new state. combineFunction will be invoked to combine two states into a new state. The final state is returned:

SELECT id, reduce_agg(value, 0, (a, b) -> a + b, (a, b) -> a + b)
FROM (
VALUES
(1, 2),
(1, 3),
(1, 4),
(2, 20),
(2, 30),
(2, 40)
) AS t(id, value)
GROUP BY id;
-- (1, 9)
-- (2, 90)

SELECT id, reduce_agg(value, 1, (a, b) -> a * b, (a, b) -> a * b)
FROM (
VALUES
(1, 2),
(1, 3),
(1, 4),
(2, 20),
(2, 30),
(2, 40)
) AS t(id, value)
GROUP BY id;
-- (1, 24)
-- (2, 24000)


The state type must be a boolean, integer, floating-point, or date/time/interval.

set_agg(x) → array<[same as input]>#

Returns an array created from the distinct input x elements.

set_union(array(T)) -> array(T)#

Returns an array of all the distinct values contained in each array of the input

Example:

SELECT set_union(elements)
FROM (
VALUES
ARRAY[1, 2, 3],
ARRAY[2, 3, 4]
) AS t(elements);


Returns ARRAY[1, 2, 3, 4]

sum(x) → [same as input]#

Returns the sum of all input values.

## Bitwise Aggregate Functions#

bitwise_and_agg(x) → bigint#

Returns the bitwise AND of all input values in 2’s complement representation.

bitwise_or_agg(x) → bigint#

Returns the bitwise OR of all input values in 2’s complement representation.

## Map Aggregate Functions#

histogram(x)#

Returns a map containing the count of the number of times each input value occurs.

map_agg(key, value)#

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.

map_union_sum(x(K, V)) -> map(K, V)#

Returns the union of all the input maps summing the values of matching keys in all the maps. All null values in the original maps are coalesced to 0.

multimap_agg(key, value)#

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 of [0.0040625, 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, percentage, accuracy) → [same as x]#

As approx_percentile(x, percentage), but with a maximum rank error of accuracy. The value of accuracy must be between zero and one (exclusive) and must be constant for all input rows. Note that a lower “accuracy” is really a lower error threshold, and thus more accurate. The default accuracy is 0.01.

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, percentages, accuracy) → array<[same as x]>#

As approx_percentile(x, percentages), but with a maximum rank error of accuracy.

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]#

As approx_percentile(x, w, percentage), but with a maximum rank error of accuracy.

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.

approx_percentile(x, w, percentages, accuracy) → array<[same as x]>#

As approx_percentile(x, w, percentages), but with a maximum rank error of accuracy.

approx_set(x) → HyperLogLog
merge(x) → HyperLogLog
khyperloglog_agg(x) → KHyperLogLog
merge(qdigest(T)) -> qdigest(T)
qdigest_agg(x) → qdigest<[same as x]>
qdigest_agg(x, w) → qdigest<[same as x]>
qdigest_agg(x, w, accuracy) → qdigest<[same as x]>
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 keys of the returned map are roughly the center of the bin, and the entry is the total weight of the bin. The algorithm is based loosely on [BenHaimTomTov2010].

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. In this case, the total weight in the returned map is the count of items in the bin.

## 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.

entropy(c) → double#

Returns the log-2 entropy of count input-values.

$\mathrm{entropy}(c) = \sum_i \left[ {c_i \over \sum_j [c_j]} \log_2\left({\sum_j [c_j] \over c_i}\right) \right].$

c must be a bigint column of non-negative values.

The function ignores any NULL count. If the sum of non-NULL counts is 0, it returns 0.

kurtosis(x) → double#

Returns the excess kurtosis of all input values. Unbiased estimate using the following expression:

$\mathrm{kurtosis}(x) = {n(n+1) \over (n-1)(n-2)(n-3)} { \sum[(x_i-\mu)^4] \over \sigma^4} -3{ (n-1)^2 \over (n-2)(n-3) },$

where $$\mu$$ is the mean, and $$\sigma$$ is the standard deviation.

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.

skewness(x) → double#

Returns the skewness of all input values.

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.

## Classification Metrics Aggregate Functions#

The following functions each measure how some metric of a binary confusion matrix changes as a function of classification thresholds. They are meant to be used in conjunction.

For example, to find the precision-recall curve, use

WITH
recall_precision AS (
SELECT
CLASSIFICATION_RECALL(10000, correct, pred) AS recalls,
CLASSIFICATION_PRECISION(10000, correct, pred) AS precisions
FROM
classification_dataset
)
SELECT
recall,
precision
FROM
recall_precision
CROSS JOIN UNNEST(recalls, precisions) AS t(recall, precision)


To get the corresponding thresholds for these values, use

WITH
recall_precision AS (
SELECT
CLASSIFICATION_THRESHOLDS(10000, correct, pred) AS thresholds,
CLASSIFICATION_RECALL(10000, correct, pred) AS recalls,
CLASSIFICATION_PRECISION(10000, correct, pred) AS precisions
FROM
classification_dataset
)
SELECT
threshold,
recall,
precision
FROM
recall_precision
CROSS JOIN UNNEST(thresholds, recalls, precisions) AS t(threshold, recall, precision)


To find the ROC curve, use

WITH
fallout_recall AS (
SELECT
CLASSIFICATION_FALLOUT(10000, correct, pred) AS fallouts,
CLASSIFICATION_RECALL(10000, correct, pred) AS recalls
FROM
classification_dataset
)
SELECT
fallout
recall,
FROM
recall_fallout
CROSS JOIN UNNEST(fallouts, recalls) AS t(fallout, recall)

classification_miss_rate(buckets, y, x, weight) → array<double>#

Computes the miss-rate with up to buckets number of buckets. Returns an array of miss-rate values.

y should be a boolean outcome value; x should be predictions, each between 0 and 1; weight should be non-negative values, indicating the weight of the instance.

The miss-rate is defined as a sequence whose $$j$$-th entry is

${ \sum_{i \;|\; x_i \leq t_j \bigwedge y_i = 1} \left[ w_i \right] \over \sum_{i \;|\; x_i \leq t_j \bigwedge y_i = 1} \left[ w_i \right] + \sum_{i \;|\; x_i > t_j \bigwedge y_i = 1} \left[ w_i \right] },$

where $$t_j$$ is the $$j$$-th smallest threshold, and $$y_i$$, $$x_i$$, and $$w_i$$ are the $$i$$-th entries of y, x, and weight, respectively.

classification_miss_rate(buckets, y, x) → array<double>#

This function is equivalent to the variant of classification_miss_rate() that takes a weight, with a per-item weight of 1.

classification_fall_out(buckets, y, x, weight) → array<double>#

Computes the fall-out with up to buckets number of buckets. Returns an array of fall-out values.

y should be a boolean outcome value; x should be predictions, each between 0 and 1; weight should be non-negative values, indicating the weight of the instance.

The fall-out is defined as a sequence whose $$j$$-th entry is

${ \sum_{i \;|\; x_i > t_j \bigwedge y_i = 0} \left[ w_i \right] \over \sum_{i \;|\; y_i = 0} \left[ w_i \right] },$

where $$t_j$$ is the $$j$$-th smallest threshold, and $$y_i$$, $$x_i$$, and $$w_i$$ are the $$i$$-th entries of y, x, and weight, respectively.

classification_fall_out(buckets, y, x) → array<double>#

This function is equivalent to the variant of classification_fall_out() that takes a weight, with a per-item weight of 1.

classification_precision(buckets, y, x, weight) → array<double>#

Computes the precision with up to buckets number of buckets. Returns an array of precision values.

y should be a boolean outcome value; x should be predictions, each between 0 and 1; weight should be non-negative values, indicating the weight of the instance.

The precision is defined as a sequence whose $$j$$-th entry is

${ \sum_{i \;|\; x_i > t_j \bigwedge y_i = 1} \left[ w_i \right] \over \sum_{i \;|\; x_i > t_j} \left[ w_i \right] },$

where $$t_j$$ is the $$j$$-th smallest threshold, and $$y_i$$, $$x_i$$, and $$w_i$$ are the $$i$$-th entries of y, x, and weight, respectively.

classification_precision(buckets, y, x) → array<double>#

This function is equivalent to the variant of classification_precision() that takes a weight, with a per-item weight of 1.

classification_recall(buckets, y, x, weight) → array<double>#

Computes the recall with up to buckets number of buckets. Returns an array of recall values.

y should be a boolean outcome value; x should be predictions, each between 0 and 1; weight should be non-negative values, indicating the weight of the instance.

The recall is defined as a sequence whose $$j$$-th entry is

${ \sum_{i \;|\; x_i > t_j \bigwedge y_i = 1} \left[ w_i \right] \over \sum_{i \;|\; y_i = 1} \left[ w_i \right] },$

where $$t_j$$ is the $$j$$-th smallest threshold, and $$y_i$$, $$x_i$$, and $$w_i$$ are the $$i$$-th entries of y, x, and weight, respectively.

classification_recall(buckets, y, x) → array<double>#

This function is equivalent to the variant of classification_recall() that takes a weight, with a per-item weight of 1.

classification_thresholds(buckets, y, x) → array<double>#

Computes the thresholds with up to buckets number of buckets. Returns an array of threshold values.

y should be a boolean outcome value; x should be predictions, each between 0 and 1.

The thresholds are defined as a sequence whose $$j$$-th entry is the $$j$$-th smallest threshold.

## Differential Entropy Functions#

The following functions approximate the binary differential entropy. That is, for a random variable $$x$$, they approximate

$h(x) = - \int x \log_2\left(f(x)\right) dx,$

where $$f(x)$$ is the partial density function of $$x$$.

differential_entropy(sample_size, x)#

Returns the approximate log-2 differential entropy from a random variable’s sample outcomes. The function internally creates a reservoir (see [Black2015]), then calculates the entropy from the sample results by approximating the derivative of the cumulative distribution (see [Alizadeh2010]).

sample_size (long) is the maximal number of reservoir samples.

x (double) is the samples.

For example, to find the differential entropy of x of data using 1000000 reservoir samples, use

SELECT
differential_entropy(1000000, x)
FROM
data


Note

If $$x$$ has a known lower and upper bound, prefer the versions taking (bucket_count, x, 1.0, "fixed_histogram_mle", min, max), or (bucket_count, x, 1.0, "fixed_histogram_jacknife", min, max), as they have better convergence.

differential_entropy(sample_size, x, weight)#

Returns the approximate log-2 differential entropy from a random variable’s sample outcomes. The function internally creates a weighted reservoir (see [Efraimidis2006]), then calculates the entropy from the sample results by approximating the derivative of the cumulative distribution (see [Alizadeh2010]).

sample_size is the maximal number of reservoir samples.

x (double) is the samples.

weight (double) is a non-negative double value indicating the weight of the sample.

For example, to find the differential entropy of x with weights weight of data using 1000000 reservoir samples, use

SELECT
differential_entropy(1000000, x, weight)
FROM
data


Note

If $$x$$ has a known lower and upper bound, prefer the versions taking (bucket_count, x, weight, "fixed_histogram_mle", min, max), or (bucket_count, x, weight, "fixed_histogram_jacknife", min, max), as they have better convergence.

differential_entropy(bucket_count, x, weight, method, min, max) → double#

Returns the approximate log-2 differential entropy from a random variable’s sample outcomes. The function internally creates a conceptual histogram of the sample values, calculates the counts, and then approximates the entropy using maximum likelihood with or without Jacknife correction, based on the method parameter. If Jacknife correction (see [Beirlant2001]) is used, the estimate is

$n H(x) - (n - 1) \sum_{i = 1}^n H\left(x_{(i)}\right)$

where $$n$$ is the length of the sequence, and $$x_{(i)}$$ is the sequence with the $$i$$-th element removed.

bucket_count (long) determines the number of histogram buckets.

x (double) is the samples.

method (varchar) is either 'fixed_histogram_mle' (for the maximum likelihood estimate) or 'fixed_histogram_jacknife' (for the jacknife-corrected maximum likelihood estimate).

min and max (both double) are the minimal and maximal values, respectively; the function will throw if there is an input outside this range.

weight (double) is the weight of the sample, and must be non-negative.

For example, to find the differential entropy of x, each between 0.0 and 1.0, with weights 1.0 of data using 1000000 bins and jacknife estimates, use

SELECT
differential_entropy(1000000, x, 1.0, 'fixed_histogram_jacknife', 0.0, 1.0)
FROM
data


To find the differential entropy of x, each between -2.0 and 2.0, with weights weight of data using 1000000 buckets and maximum-likelihood estimates, use

SELECT
differential_entropy(1000000, x, weight, 'fixed_histogram_mle', -2.0, 2.0)
FROM
data


Note

If $$x$$ doesn’t have known lower and upper bounds, prefer the versions taking (sample_size, x) (unweighted case) or (sample_size, x, weight) (weighted case), as they use reservoir sampling which doesn’t require a known range for samples.

Otherwise, if the number of distinct weights is low, especially if the number of samples is low, consider using the version taking (bucket_count, x, weight, "fixed_histogram_jacknife", min, max), as jacknife bias correction, is better than maximum likelihood estimation. However, if the number of distinct weights is high, consider using the version taking (bucket_count, x, weight, "fixed_histogram_mle", min, max), as this will reduce memory and running time.

approx_most_frequent(buckets, value, capacity) → map<[same as value], bigint>#

Computes the top frequent values up to buckets elements approximately. Approximate estimation of the function enables us to pick up the frequent values with less memory. Larger capacity improves the accuracy of underlying algorithm with sacrificing the memory capacity. The returned value is a map containing the top elements with corresponding estimated frequency.

The error of the function depends on the permutation of the values and its cardinality. We can set the capacity same as the cardinality of the underlying data to achieve the least error.

buckets and capacity must be bigint. value can be numeric or string type.

The function uses the stream summary data structure proposed in the paper Efficient computation of frequent and top-k elements in data streams by A.Metwalley, D.Agrawl and A.Abbadi.

Alizadeh Noughabi, Hadi & Arghami, N. (2010). “A New Estimator of Entropy”.

Beirlant2001

Beirlant, Dudewicz, Gyorfi, and van der Meulen, “Nonparametric entropy estimation: an overview”, (2001)

BenHaimTomTov2010

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

Black2015

Black, Paul E. (26 January 2015). “Reservoir sampling”. Dictionary of Algorithms and Data Structures.

Efraimidis2006

Efraimidis, Pavlos S.; Spirakis, Paul G. (2006-03-16). “Weighted random sampling with a reservoir”. Information Processing Letters. 97 (5): 181–185.