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.
Ordering during aggregation#
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)
Filtering during aggregation#
The FILTER
keyword can be used to remove rows from aggregation processing
with a condition expressed using a WHERE
clause. This is evaluated for each
row before it is used in the aggregation and is supported for all aggregate
functions.
aggregate_function(...) FILTER (WHERE <condition>)
A common and very useful example is to use FILTER
to remove nulls from
consideration when using array_agg
:
SELECT array_agg(name) FILTER (WHERE name IS NOT NULL)
FROM region;
As another example, imagine you want to add a condition on the count for Iris flowers, modifying the following query:
SELECT species,
count(*) AS count
FROM iris
GROUP BY species;
species  count
+
setosa  50
virginica  50
versicolor  50
If you just use a normal WHERE
statement you loose information:
SELECT species,
count(*) AS count
FROM iris
WHERE petal_length_cm > 4
GROUP BY species;
species  count
+
virginica  50
versicolor  34
Using a filter you retain all information:
SELECT species,
count(*) FILTER (where petal_length_cm > 4) AS count
FROM iris
GROUP BY species;
species  count
+
virginica  50
setosa  0
versicolor  34
General aggregate functions#

arbitrary
(x) → [same as input]# Returns an arbitrary nonnull 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 isTRUE
, otherwiseFALSE
.

bool_or
(boolean) → boolean# Returns
TRUE
if any input value isTRUE
, otherwiseFALSE
.

checksum
(x) → varbinary# Returns an orderinsensitive checksum of the given values.

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

count
(x) → bigint Returns the number of nonnull input values.

count_if
(x) → bigint# Returns the number of
TRUE
input values. This function is equivalent tocount(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
(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 ofx
.

max_by
(x, y) → [same as x]# Returns the value of
x
associated with the maximum value ofy
over all input values.

max_by
(x, y, n) → array<[same as x]> Returns
n
values ofx
associated with then
largest of all input values ofy
in descending order ofy
.

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 ofx
.

min_by
(x, y) → [same as x]# Returns the value of
x
associated with the minimum value ofy
over all input values.

min_by
(x, y, n) → array<[same as x]> Returns
n
values ofx
associated with then
smallest of all input values ofy
in ascending order ofy
.

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.
For example, take the following histogram function that creates multiple maps from the Iris dataset:
SELECT histogram(floor(petal_length_cm)) petal_data FROM memory.default.iris GROUP BY species; petal_data  {4.0=6, 5.0=33, 6.0=11}  {4.0=37, 5.0=2, 3.0=11}  {1.0=50}
You can combine these maps using
map_union
:SELECT map_union(petal_data) petal_data_union FROM ( SELECT histogram(floor(petal_length_cm)) petal_data FROM memory.default.iris GROUP BY species ); petal_data_union {4.0=6, 5.0=2, 6.0=11, 1.0=50, 3.0=11}

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 thate
be in the range of[0.0040625, 0.26000]
.

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. Largercapacity
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
andcapacity
must bebigint
.value
can be numeric or string type.The function uses the stream summary data structure proposed in the paper Efficient Computation of Frequent and Topk Elements in Data Streams by A. Metwalley, D. Agrawl and A. Abbadi.

approx_percentile
(x, percentage) → [same as x]# Returns the approximate percentile for all input values of
x
at the givenpercentage
. The value ofpercentage
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 thepercentages
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 peritem weightw
at the percentagepercentage
. Weights must be strictly positive. Integervalue weights can be thought of as a replication count for the valuex
in the percentile set. The value ofpercentage
must be between zero and one and 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 peritem weightw
at each of the given percentages specified in the array. Weights must be strictly positive. Integervalue weights can be thought of as a replication count for the valuex
in the percentile set. Each element of thepercentages
array must be between zero and one, and the array must be constant for all input rows.

approx_set
(x) → HyperLogLog

merge
(x) → HyperLogLog

merge
(qdigest(T)) > qdigest(T)

merge
(tdigest) → tdigest See TDigest functions.

numeric_histogram
(buckets, value) → map<double, double> Computes an approximate histogram with up to
buckets
number of buckets for allvalue
s. This function is equivalent to the variant ofnumeric_histogram()
that takes aweight
, with a peritem weight of1
.

numeric_histogram
(buckets, value, weight) → map<double, double># Computes an approximate histogram with up to
buckets
number of buckets for allvalue
s with a peritem weight ofweight
. The algorithm is based loosely on:Yael BenHaim and Elad TomTov, "A streaming parallel decision tree algorithm", J. Machine Learning Research 11 (2010), pp. 849872.
buckets
must be abigint
.value
andweight
must be numeric.

qdigest_agg
(x) > qdigest([same as x])

qdigest_agg
(x, w) > qdigest([same as x])

qdigest_agg
(x, w, accuracy) > qdigest([same as x])

tdigest_agg
(x) → tdigest See TDigest functions.

tdigest_agg
(x, w) → tdigest See TDigest functions.
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.

kurtosis
(x) → double# Returns the excess kurtosis of all input values. Unbiased estimate using the following expression:
kurtosis(x) = n(n+1)/((n1)(n2)(n3))sum[(x_imean)^4]/stddev(x)^43(n1)^2/((n2)(n3))

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.
Lambda aggregate functions#

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 nonnull input value. In addition to taking the input value,inputFunction
takes the current state, initiallyinitialState
, 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, 3), (1, 4), (1, 5), (2, 6), (2, 7) ) AS t(id, value) GROUP BY id;  (1, 12)  (2, 13) SELECT id, reduce_agg(value, 1, (a, b) > a * b, (a, b) > a * b) FROM ( VALUES (1, 3), (1, 4), (1, 5), (2, 6), (2, 7) ) AS t(id, value) GROUP BY id;  (1, 60)  (2, 42)
The state type must be a boolean, integer, floatingpoint, or date/time/interval.