Aggregate Functions

count

Counts the number of rows or not-NULL values.

ClickHouse supports the following syntaxes for count:

count(expr) or COUNT(DISTINCT expr).
count() or COUNT(*). The count() syntax is ClickHouse-specific.

Arguments

The function can take:

Zero parameters.
One expression.

Returned value

If the function is called without parameters it counts the number of rows.
If the expression is passed, then the function counts how many times this expression returned not null. If the expression returns a Nullable-type value, then the result of count stays not Nullable. The function returns 0 if the expression returned NULL for all the rows.

In both cases the type of the returned value is UInt64.

Details

ClickHouse supports the COUNT(DISTINCT ...) syntax. The behavior of this construction depends on the count_distinct_implementation setting. It defines which of the uniq* functions is used to perform the operation. The default is the uniqExact function.

The SELECT count() FROM table query is optimized by default using metadata from MergeTree. If you need to use row-level security, disable optimization using the optimize_trivial_count_query setting.

However SELECT count(nullable_column) FROM table query can be optimized by enabling the optimize_functions_to_subcolumns setting. With optimize_functions_to_subcolumns = 1 the function reads only null subcolumn instead of reading and processing the whole column data. The query SELECT count(n) FROM table transforms to SELECT sum(NOT n.null) FROM table.

Examples

Example 1:

SELECT count() FROM t

┌─count()─┐
│       5 │
└─────────┘

Example 2:

SELECT name, value FROM system.settings WHERE name = 'count_distinct_implementation'

┌─name──────────────────────────┬─value─────┐
│ count_distinct_implementation │ uniqExact │
└───────────────────────────────┴───────────┘

SELECT count(DISTINCT num) FROM t

┌─uniqExact(num)─┐
│              3 │
└────────────────┘

This example shows that count(DISTINCT num) is performed by the uniqExact function according to the count_distinct_implementation setting value.

min

Aggregate function that calculates the minimum across a group of values.

Example:

SELECT min(salary) FROM employees;

SELECT department, min(salary) FROM employees GROUP BY department;

If you need non-aggregate function to choose a minimum of two values, see least:

SELECT least(a, b) FROM table;

max

Aggregate function that calculates the maximum across a group of values.

Example:

SELECT max(salary) FROM employees;

SELECT department, max(salary) FROM employees GROUP BY department;

If you need non-aggregate function to choose a maximum of two values, see greatest:

SELECT greatest(a, b) FROM table;

sum

Calculates the sum. Only works for numbers.

avg

Calculates the arithmetic mean.

Syntax

avg(x)

Arguments

x — input values, must be Integer, Float, or Decimal.

Returned value

The arithmetic mean, always as Float64.
NaN if the input parameter x is empty.

Example

Query:

SELECT avg(x) FROM values('x Int8', 0, 1, 2, 3, 4, 5);

Result:

┌─avg(x)─┐
│    2.5 │
└────────┘

Example

Create a temp table:

Query:

CREATE table test (t UInt8) ENGINE = Memory;

Get the arithmetic mean:

Query:

SELECT avg(t) FROM test;

Result:

┌─avg(x)─┐
│    nan │
└────────┘

any

Selects the first encountered (non-NULL) value, unless all rows have NULL values in that column. The query can be executed in any order and even in a different order each time, so the result of this function is indeterminate. To get a determinate result, you can use the ‘min’ or ‘max’ function instead of ‘any’.

In some cases, you can rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

When a SELECT query has the GROUP BY clause or at least one aggregate function, ClickHouse (in contrast to MySQL) requires that all expressions in the SELECT, HAVING, and ORDER BY clauses be calculated from keys or from aggregate functions. In other words, each column selected from the table must be used either in keys or inside aggregate functions. To get behavior like in MySQL, you can put the other columns in the any aggregate function.

stddevPop

The result is equal to the square root of varPop.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the stddevPopStable function. It works slower but provides a lower computational error.

stddevSamp

The result is equal to the square root of varSamp.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the stddevSampStable function. It works slower but provides a lower computational error.

varPop(x)

Calculates the amount Σ((x - x̅)^2) / n, where n is the sample size and x̅is the average value of x.

In other words, dispersion for a set of values. Returns Float64.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the varPopStable function. It works slower but provides a lower computational error.

varSamp

Calculates the amount Σ((x - x̅)^2) / (n - 1), where n is the sample size and x̅is the average value of x.

It represents an unbiased estimate of the variance of a random variable if passed values from its sample.

Returns Float64. When n <= 1, returns +∞.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the varSampStable function. It works slower but provides a lower computational error.

covarPop

Syntax: covarPop(x, y)

Calculates the value of Σ((x - x̅)(y - y̅)) / n.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the covarPopStable function. It works slower but provides a lower computational error.

covarSamp

Calculates the value of Σ((x - x̅)(y - y̅)) / (n - 1).

Returns Float64. When n <= 1, returns +∞.

NOTE

This function uses a numerically unstable algorithm. If you need numerical stability in calculations, use the covarSampStable function. It works slower but provides a lower computational error.

anyHeavy

Selects a frequently occurring value using the heavy hitters algorithm. If there is a value that occurs more than in half the cases in each of the query’s execution threads, this value is returned. Normally, the result is nondeterministic.

anyHeavy(column)

Arguments

column – The column name.

Example

Take the OnTime data set and select any frequently occurring value in the AirlineID column.

SELECT anyHeavy(AirlineID) AS res
FROM ontime

┌───res─┐
│ 19690 │
└───────┘

anyLast

Selects the last value encountered. The result is just as indeterminate as for the any function.

argMin

Calculates the arg value for a minimum val value. If there are several different values of arg for minimum values of val, returns the first of these values encountered.

Syntax

argMin(arg, val)

Arguments

arg — Argument.
val — Value.

Returned value

arg value that corresponds to minimum val value.

Type: matches arg type.

Example

Input table:

┌─user─────┬─salary─┐
│ director │   5000 │
│ manager  │   3000 │
│ worker   │   1000 │
└──────────┴────────┘

Query:

SELECT argMin(user, salary) FROM salary

Result:

┌─argMin(user, salary)─┐
│ worker               │
└──────────────────────┘

avgWeighted

Calculates the weighted arithmetic mean.

Syntax

avgWeighted(x, weight)

Arguments

x — Values.
weight — Weights of the values.

x and weight must both be Integer, floating-point, or Decimal, but may have different types.

Returned value

NaN if all the weights are equal to 0 or the supplied weights parameter is empty.
Weighted mean otherwise.

Return type is always Float64.

Example

Query:

SELECT avgWeighted(x, w)
FROM values('x Int8, w Int8', (4, 1), (1, 0), (10, 2))

Result:

┌─avgWeighted(x, weight)─┐
│                      8 │
└────────────────────────┘

Example

Query:

SELECT avgWeighted(x, w)
FROM values('x Int8, w Float64', (4, 1), (1, 0), (10, 2))

Result:

┌─avgWeighted(x, weight)─┐
│                      8 │
└────────────────────────┘

Example

Query:

SELECT avgWeighted(x, w)
FROM values('x Int8, w Int8', (0, 0), (1, 0), (10, 0))

Result:

┌─avgWeighted(x, weight)─┐
│                    nan │
└────────────────────────┘

Example

Query:

CREATE table test (t UInt8) ENGINE = Memory;
SELECT avgWeighted(t) FROM test

Result:

┌─avgWeighted(x, weight)─┐
│                    nan │
└────────────────────────┘

topK

Returns an array of the approximately most frequent values in the specified column. The resulting array is sorted in descending order of approximate frequency of values (not by the values themselves).

Implements the Filtered Space-Saving algorithm for analyzing TopK, based on the reduce-and-combine algorithm from Parallel Space Saving.

topK(N)(column)

This function does not provide a guaranteed result. In certain situations, errors might occur and it might return frequent values that aren’t the most frequent values.

We recommend using the N < 10 value; performance is reduced with large N values. Maximum value of N = 65536.

Arguments

N – The number of elements to return.

If the parameter is omitted, default value 10 is used.

Arguments

x – The value to calculate frequency.

Example

Take the OnTime data set and select the three most frequently occurring values in the AirlineID column.

SELECT topK(3)(AirlineID) AS res
FROM ontime

┌─res─────────────────┐
│ [19393,19790,19805] │
└─────────────────────┘

topKWeighted

Syntax

topKWeighted(N)(x, weight)

Arguments

N — The number of elements to return.
x — The value.
weight — The weight. Every value is accounted weight times for frequency calculation. UInt64.

Returned value

Returns an array of the values with maximum approximate sum of weights.

Example

Query:

SELECT topKWeighted(10)(number, number) FROM numbers(1000)

Result:

┌─topKWeighted(10)(number, number)──────────┐
│ [999,998,997,996,995,994,993,992,991,990] │
└───────────────────────────────────────────┘

See Also

topK

groupArray

Syntax: groupArray(x) or groupArray(max_size)(x)

Creates an array of argument values. Values can be added to the array in any (indeterminate) order.

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. For example, groupArray(1)(x) is equivalent to [any (x)].

In some cases, you can still rely on the order of execution. This applies to cases when SELECT comes from a subquery that uses ORDER BY.

Example

SELECT * FROM default.ck;

┌─id─┬─name─────┐
│  1 │ zhangsan │
│  1 │ ᴺᵁᴸᴸ     │
│  1 │ lisi     │
│  2 │ wangwu   │
└────┴──────────┘

Query:

select id, groupArray(10)(name) from default.ck group by id;

Result:

┌─id─┬─groupArray(10)(name)─┐
│  1 │ ['zhangsan','lisi']  │
│  2 │ ['wangwu']           │
└────┴──────────────────────┘

The groupArray function will remove ᴺᵁᴸᴸ value based on the above results.

groupUniqArray

Syntax: groupUniqArray(x) or groupUniqArray(max_size)(x)

Creates an array from different argument values. Memory consumption is the same as for the uniqExact function.

The second version (with the max_size parameter) limits the size of the resulting array to max_size elements. For example, groupUniqArray(1)(x) is equivalent to [any(x)].

groupArrayInsertAt

Inserts a value into the array at the specified position.

Syntax

groupArrayInsertAt(default_x, size)(x, pos)

If in one query several values are inserted into the same position, the function behaves in the following ways:

If a query is executed in a single thread, the first one of the inserted values is used.
If a query is executed in multiple threads, the resulting value is an undetermined one of the inserted values.

Arguments

x — Value to be inserted. Expression resulting in one of the supported data types.
pos — Position at which the specified element x is to be inserted. Index numbering in the array starts from zero. UInt32.
default_x — Default value for substituting in empty positions. Optional parameter. Expression resulting in the data type configured for the x parameter. If default_x is not defined, the default values are used.
size — Length of the resulting array. Optional parameter. When using this parameter, the default value default_x must be specified. UInt32.

Returned value

Array with inserted values.

Type: Array.

Example

Query:

SELECT groupArrayInsertAt(toString(number), number * 2) FROM numbers(5);

Result:

┌─groupArrayInsertAt(toString(number), multiply(number, 2))─┐
│ ['0','','1','','2','','3','','4']                         │
└───────────────────────────────────────────────────────────┘

Query:

SELECT groupArrayInsertAt('-')(toString(number), number * 2) FROM numbers(5);

Result:

┌─groupArrayInsertAt('-')(toString(number), multiply(number, 2))─┐
│ ['0','-','1','-','2','-','3','-','4']                          │
└────────────────────────────────────────────────────────────────┘

Query:

SELECT groupArrayInsertAt('-', 5)(toString(number), number * 2) FROM numbers(5);

Result:

┌─groupArrayInsertAt('-', 5)(toString(number), multiply(number, 2))─┐
│ ['0','-','1','-','2']                                             │
└───────────────────────────────────────────────────────────────────┘

Multi-threaded insertion of elements into one position.

Query:

SELECT groupArrayInsertAt(number, 0) FROM numbers_mt(10) SETTINGS max_block_size = 1;

As a result of this query you get random integer in the [0,9] range. For example:

┌─groupArrayInsertAt(number, 0)─┐
│ [7]                           │
└───────────────────────────────┘

groupArrayMovingAvg

Calculates the moving average of input values.

groupArrayMovingAvg(numbers_for_summing)
groupArrayMovingAvg(window_size)(numbers_for_summing)

The function can take the window size as a parameter. If left unspecified, the function takes the window size equal to the number of rows in the column.

Arguments

numbers_for_summing — Expression resulting in a numeric data type value.
window_size — Size of the calculation window.

Returned values

Array of the same size and type as the input data.

The function uses rounding towards zero. It truncates the decimal places insignificant for the resulting data type.

Example

The sample table b:

CREATE TABLE t
(
    `int` UInt8,
    `float` Float32,
    `dec` Decimal32(2)
)
ENGINE = TinyLog

┌─int─┬─float─┬──dec─┐
│   1 │   1.1 │ 1.10 │
│   2 │   2.2 │ 2.20 │
│   4 │   4.4 │ 4.40 │
│   7 │  7.77 │ 7.77 │
└─────┴───────┴──────┘

The queries:

SELECT
    groupArrayMovingAvg(int) AS I,
    groupArrayMovingAvg(float) AS F,
    groupArrayMovingAvg(dec) AS D
FROM t

┌─I─────────┬─F───────────────────────────────────┬─D─────────────────────┐
│ [0,0,1,3] │ [0.275,0.82500005,1.9250001,3.8675] │ [0.27,0.82,1.92,3.86] │
└───────────┴─────────────────────────────────────┴───────────────────────┘

SELECT
    groupArrayMovingAvg(2)(int) AS I,
    groupArrayMovingAvg(2)(float) AS F,
    groupArrayMovingAvg(2)(dec) AS D
FROM t

┌─I─────────┬─F────────────────────────────────┬─D─────────────────────┐
│ [0,1,3,5] │ [0.55,1.6500001,3.3000002,6.085] │ [0.55,1.65,3.30,6.08] │
└───────────┴──────────────────────────────────┴───────────────────────┘

sumWithOverflow

Computes the sum of the numbers, using the same data type for the result as for the input parameters. If the sum exceeds the maximum value for this data type, it is calculated with overflow.

Only works for numbers.

sumMap

Syntax: sumMap(key, value) or sumMap(Tuple(key, value))

Totals the value array according to the keys specified in the key array.

Passing tuple of keys and values arrays is a synonym to passing two arrays of keys and values.

The number of elements in key and value must be the same for each row that is totaled.

Returns a tuple of two arrays: keys in sorted order, and values summed for the corresponding keys.

Example:

CREATE TABLE sum_map(
    date Date,
    timeslot DateTime,
    statusMap Nested(
        status UInt16,
        requests UInt64
    ),
    statusMapTuple Tuple(Array(Int32), Array(Int32))
) ENGINE = Log;
INSERT INTO sum_map VALUES
    ('2000-01-01', '2000-01-01 00:00:00', [1, 2, 3], [10, 10, 10], ([1, 2, 3], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:00:00', [3, 4, 5], [10, 10, 10], ([3, 4, 5], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:01:00', [4, 5, 6], [10, 10, 10], ([4, 5, 6], [10, 10, 10])),
    ('2000-01-01', '2000-01-01 00:01:00', [6, 7, 8], [10, 10, 10], ([6, 7, 8], [10, 10, 10]));

SELECT
    timeslot,
    sumMap(statusMap.status, statusMap.requests),
    sumMap(statusMapTuple)
FROM sum_map
GROUP BY timeslot

┌────────────timeslot─┬─sumMap(statusMap.status, statusMap.requests)─┬─sumMap(statusMapTuple)─────────┐
│ 2000-01-01 00:00:00 │ ([1,2,3,4,5],[10,10,20,10,10])               │ ([1,2,3,4,5],[10,10,20,10,10]) │
│ 2000-01-01 00:01:00 │ ([4,5,6,7,8],[10,10,20,10,10])               │ ([4,5,6,7,8],[10,10,20,10,10]) │
└─────────────────────┴──────────────────────────────────────────────┴────────────────────────────────┘

minMap

Syntax: minMap(key, value) or minMap(Tuple(key, value))

Calculates the minimum from value array according to the keys specified in the key array.

Passing a tuple of keys and value arrays is identical to passing two arrays of keys and values.

The number of elements in key and value must be the same for each row that is totaled.

Returns a tuple of two arrays: keys in sorted order, and values calculated for the corresponding keys.

Example:

SELECT minMap(a, b)
FROM values('a Array(Int32), b Array(Int64)', ([1, 2], [2, 2]), ([2, 3], [1, 1]))

┌─minMap(a, b)──────┐
│ ([1,2,3],[2,1,1]) │
└───────────────────┘

maxMap

Syntax: maxMap(key, value) or maxMap(Tuple(key, value))

Calculates the maximum from value array according to the keys specified in the key array.

Passing a tuple of keys and value arrays is identical to passing two arrays of keys and values.

The number of elements in key and value must be the same for each row that is totaled.

Returns a tuple of two arrays: keys and values calculated for the corresponding keys.

Example:

SELECT maxMap(a, b)
FROM values('a Array(Int32), b Array(Int64)', ([1, 2], [2, 2]), ([2, 3], [1, 1]))

┌─maxMap(a, b)──────┐
│ ([1,2,3],[2,2,1]) │
└───────────────────┘

skewSamp

Computes the sample skewness of a sequence.

It represents an unbiased estimate of the skewness of a random variable if passed values form its sample.

skewSamp(expr)

Arguments

expr — Expression returning a number.

Returned value

The skewness of the given distribution. Type — Float64. If n <= 1 (n is the size of the sample), then the function returns nan.

Example

SELECT skewSamp(value) FROM series_with_value_column;

skewPop

Computes the skewness of a sequence.

skewPop(expr)

Arguments

expr — Expression returning a number.

Returned value

The skewness of the given distribution. Type — Float64

Example

SELECT skewPop(value) FROM series_with_value_column;

kurtSamp

Computes the sample kurtosis of a sequence.

It represents an unbiased estimate of the kurtosis of a random variable if passed values form its sample.

kurtSamp(expr)

Arguments

expr — Expression returning a number.

Returned value

The kurtosis of the given distribution. Type — Float64. If n <= 1 (n is a size of the sample), then the function returns nan.

Example

SELECT kurtSamp(value) FROM series_with_value_column;

kurtPop

Computes the kurtosis of a sequence.

kurtPop(expr)

Arguments

expr — Expression returning a number.

Returned value

The kurtosis of the given distribution. Type — Float64

Example

SELECT kurtPop(value) FROM series_with_value_column;

uniq

Calculates the approximate number of different values of the argument.

uniq(x[, ...])

Arguments

The function takes a variable number of parameters. Parameters can be Tuple, Array, Date, DateTime, String, or numeric types.

Returned value

A UInt64-type number.

Implementation details

Function:

Calculates a hash for all parameters in the aggregate, then uses it in calculations.
Uses an adaptive sampling algorithm. For the calculation state, the function uses a sample of element hash values up to 65536. This algorithm is very accurate and very efficient on the CPU. When the query contains several of these functions, using uniq is almost as fast as using other aggregate functions.
Provides the result deterministically (it does not depend on the query processing order).

We recommend using this function in almost all scenarios.

uniqExact

Calculates the exact number of different argument values.

uniqExact(x[, ...])

Use the uniqExact function if you absolutely need an exact result. Otherwise use the uniq function.

The uniqExact function uses more memory than uniq, because the size of the state has unbounded growth as the number of different values increases.

Arguments

The function takes a variable number of parameters. Parameters can be Tuple, Array, Date, DateTime, String, or numeric types.

uniqCombined

Calculates the approximate number of different argument values.

uniqCombined(HLL_precision)(x[, ...])

The uniqCombined function is a good choice for calculating the number of different values.

Arguments

The function takes a variable number of parameters. Parameters can be Tuple, Array, Date, DateTime, String, or numeric types.

HLL_precision is the base-2 logarithm of the number of cells in HyperLogLog. Optional, you can use the function as uniqCombined(x[, ...]). The default value for HLL_precision is 17, which is effectively 96 KiB of space (2^17 cells, 6 bits each).

Returned value

A number UInt64-type number.

Implementation details

Function:

Calculates a hash (64-bit hash for String and 32-bit otherwise) for all parameters in the aggregate, then uses it in calculations.

Uses a combination of three algorithms: array, hash table, and HyperLogLog with an error correction table.

For a small number of distinct elements, an array is used. When the set size is larger, a hash table is used. For a larger number of elements, HyperLogLog is used, which will occupy a fixed amount of memory.

Provides the result deterministically (it does not depend on the query processing order).

NOTE

Since it uses 32-bit hash for non-String type, the result will have very high error for cardinalities significantly larger than UINT_MAX (error will raise quickly after a few tens of billions of distinct values), hence in this case you should use uniqCombined64

Compared to the uniq function, the uniqCombined:

Consumes several times less memory.
Calculates with several times higher accuracy.
Usually has slightly lower performance. In some scenarios, uniqCombined can perform better than uniq, for example, with distributed queries that transmit a large number of aggregation states over the network.

uniqCombined64

Same as uniqCombined, but uses 64-bit hash for all data types.

uniqHLL12

Calculates the approximate number of different argument values, using the HyperLogLog algorithm.

uniqHLL12(x[, ...])

Arguments

The function takes a variable number of parameters. Parameters can be Tuple, Array, Date, DateTime, String, or numeric types.

Returned value

A UInt64-type number.

Implementation details

Function:

Calculates a hash for all parameters in the aggregate, then uses it in calculations.

Uses the HyperLogLog algorithm to approximate the number of different argument values.

2^12 5-bit cells are used. The size of the state is slightly more than 2.5 KB. The result is not very accurate (up to ~10% error) for small data sets (<10K elements). However, the result is fairly accurate for high-cardinality data sets (10K-100M), with a maximum error of ~1.6%. Starting from 100M, the estimation error increases, and the function will return very inaccurate results for data sets with extremely high cardinality (1B+ elements).

Provides the determinate result (it does not depend on the query processing order).

We do not recommend using this function. In most cases, use the uniq or uniqCombined function.

quantile

Computes an approximate quantile of a numeric data sequence.

This function applies reservoir sampling with a reservoir size up to 8192 and a random number generator for sampling. The result is non-deterministic. To get an exact quantile, use the quantileExact function.

When using multiple quantile* functions with different levels in a query, the internal states are not combined (that is, the query works less efficiently than it could). In this case, use the quantiles function.

Note that for an empty numeric sequence, quantile will return NaN, but its quantile* variants will return either NaN or a default value for the sequence type, depending on the variant.

Syntax

quantile(level)(expr)

Alias: median.

Arguments

level — Level of quantile. Optional parameter. Constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]. Default value: 0.5. At level=0.5 the function calculates median.
expr — Expression over the column values resulting in numeric data types, Date or DateTime.

Returned value

Approximate quantile of the specified level.

Type:

Float64 for numeric data type input.
Date if input values have the Date type.
DateTime if input values have the DateTime type.

Example

Input table:

┌─val─┐
│   1 │
│   1 │
│   2 │
│   3 │
└─────┘

Query:

SELECT quantile(val) FROM t

Result:

┌─quantile(val)─┐
│           1.5 │
└───────────────┘

quantiles

Syntax: quantiles(level1, level2, …)(x)

All the quantile functions also have corresponding quantiles functions: quantiles, quantilesDeterministic, quantilesTiming, quantilesTimingWeighted, quantilesExact, quantilesExactWeighted, quantilesTDigest, quantilesBFloat16. These functions calculate all the quantiles of the listed levels in one pass, and return an array of the resulting values.

quantilesExactExclusive

Exactly computes the quantiles of a numeric data sequence.

To get exact value, all the passed values are combined into an array, which is then partially sorted. Therefore, the function consumes O(n) memory, where n is a number of values that were passed. However, for a small number of values, the function is very effective.

This function is equivalent to PERCENTILE.EXC Excel function, (type R6).

Works more efficiently with sets of levels than quantileExactExclusive.

Syntax

quantilesExactExclusive(level1, level2, ...)(expr)

Arguments

expr — Expression over the column values resulting in numeric data types, Date or DateTime.

Parameters

level — Levels of quantiles. Possible values: (0, 1) — bounds not included. Float.

Returned value

Array of quantiles of the specified levels.

Type of array values:

Float64 for numeric data type input.
Date if input values have the Date type.
DateTime if input values have the DateTime type.

Example

Query:

CREATE TABLE num AS numbers(1000);

SELECT quantilesExactExclusive(0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.999)(x) FROM (SELECT number AS x FROM num);

Result:

┌─quantilesExactExclusive(0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.999)(x)─┐
│ [249.25,499.5,749.75,899.9,949.9499999999999,989.99,998.999]        │
└─────────────────────────────────────────────────────────────────────┘

quantilesExactInclusive

Exactly computes the quantiles of a numeric data sequence.

This function is equivalent to PERCENTILE.INC Excel function, (type R7).

Works more efficiently with sets of levels than quantileExactInclusive.

Syntax

quantilesExactInclusive(level1, level2, ...)(expr)

Arguments

expr — Expression over the column values resulting in numeric data types, Date or DateTime.

Parameters

level — Levels of quantiles. Possible values: [0, 1] — bounds included. Float.

Returned value

Array of quantiles of the specified levels.

Type of array values:

Float64 for numeric data type input.
Date if input values have the Date type.
DateTime if input values have the DateTime type.

Example

Query:

CREATE TABLE num AS numbers(1000);

SELECT quantilesExactInclusive(0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.999)(x) FROM (SELECT number AS x FROM num);

Result:

┌─quantilesExactInclusive(0.25, 0.5, 0.75, 0.9, 0.95, 0.99, 0.999)(x)─┐
│ [249.75,499.5,749.25,899.1,949.05,989.01,998.001]                   │

quantileExactWeighted

Exactly computes the quantile of a numeric data sequence, taking into account the weight of each element.

To get exact value, all the passed values are combined into an array, which is then partially sorted. Each value is counted with its weight, as if it is present weight times. A hash table is used in the algorithm. Because of this, if the passed values are frequently repeated, the function consumes less RAM than quantileExact. You can use this function instead of quantileExact and specify the weight 1.

Syntax

quantileExactWeighted(level)(expr, weight)

Alias: medianExactWeighted.

Arguments

level — Level of quantile. Optional parameter. Constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]. Default value: 0.5. At level=0.5 the function calculates median.
expr — Expression over the column values resulting in numeric data types, Date or DateTime.
weight — Column with weights of sequence members. Weight is a number of value occurrences.

Returned value

Quantile of the specified level.

Type:

Float64 for numeric data type input.
Date if input values have the Date type.
DateTime if input values have the DateTime type.

Example

Input table:

┌─n─┬─val─┐
│ 0 │   3 │
│ 1 │   2 │
│ 2 │   1 │
│ 5 │   4 │
└───┴─────┘

Query:

SELECT quantileExactWeighted(n, val) FROM t

Result:

┌─quantileExactWeighted(n, val)─┐
│                             1 │
└───────────────────────────────┘

quantileTiming

With the determined precision computes the quantile of a numeric data sequence.

The result is deterministic (it does not depend on the query processing order). The function is optimized for working with sequences which describe distributions like loading web pages times or backend response times.

Syntax

quantileTiming(level)(expr)

Alias: medianTiming.

Arguments

level — Level of quantile. Optional parameter. Constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]. Default value: 0.5. At level=0.5 the function calculates median.
expr — Expression over a column values returning a Float*-type number.
- If negative values are passed to the function, the behavior is undefined.
- If the value is greater than 30,000 (a page loading time of more than 30 seconds), it is assumed to be 30,000.

Accuracy

The calculation is accurate if:

Total number of values does not exceed 5670.
Total number of values exceeds 5670, but the page loading time is less than 1024ms.

Otherwise, the result of the calculation is rounded to the nearest multiple of 16 ms.

NOTE

For calculating page loading time quantiles, this function is more effective and accurate than quantile.

Returned value

Quantile of the specified level.

Type: Float32.

NOTE

If no values are passed to the function (when using quantileTimingIf), NaN is returned. The purpose of this is to differentiate these cases from cases that result in zero. See ORDER BY clause for notes on sorting NaN values.

Example

Input table:

┌─response_time─┐
│            72 │
│           112 │
│           126 │
│           145 │
│           104 │
│           242 │
│           313 │
│           168 │
│           108 │
└───────────────┘

Query:

SELECT quantileTiming(response_time) FROM t

Result:

┌─quantileTiming(response_time)─┐
│                           126 │
└───────────────────────────────┘

quantileTimingWeighted

With the determined precision computes the quantile of a numeric data sequence according to the weight of each sequence member.

Syntax

quantileTimingWeighted(level)(expr, weight)

Alias: medianTimingWeighted.

Arguments

level — Level of quantile. Optional parameter. Constant floating-point number from 0 to 1. We recommend using a level value in the range of [0.01, 0.99]. Default value: 0.5. At level=0.5 the function calculates median.

expr — Expression over a column values returning a Float*-type number.

- If negative values are passed to the function, the behavior is undefined.
- If the value is greater than 30,000 (a page loading time of more than 30 seconds), it is assumed to be 30,000.

weight — Column with weights of sequence elements. Weight is a number of value occurrences.

Accuracy

The calculation is accurate if:

Total number of values does not exceed 5670.
Total number of values exceeds 5670, but the page loading time is less than 1024ms.

Otherwise, the result of the calculation is rounded to the nearest multiple of 16 ms.

NOTE

For calculating page loading time quantiles, this function is more effective and accurate than quantile.

Returned value

Quantile of the specified level.

Type: Float32.

NOTE

Example

Input table:

┌─response_time─┬─weight─┐
│            68 │      1 │
│           104 │      2 │
│           112 │      3 │
│           126 │      2 │
│           138 │      1 │
│           162 │      1 │
└───────────────┴────────┘

Query:

SELECT quantileTimingWeighted(response_time, weight) FROM t

Result:

┌─quantileTimingWeighted(response_time, weight)─┐
│                                           112 │
└───────────────────────────────────────────────┘

quantilesTimingWeighted

Same as quantileTimingWeighted, but accept multiple parameters with quantile levels and return an Array filled with many values of that quantiles.

Example

Input table:

┌─response_time─┬─weight─┐
│            68 │      1 │
│           104 │      2 │
│           112 │      3 │
│           126 │      2 │
│           138 │      1 │
│           162 │      1 │
└───────────────┴────────┘

Query:

SELECT quantilesTimingWeighted(0,5, 0.99)(response_time, weight) FROM t

Result:

┌─quantilesTimingWeighted(0.5, 0.99)(response_time, weight)─┐
│ [112,162]                                                 │
└───────────────────────────────────────────────────────────┘

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