Aggregate functions

This page describes the available functions to assist with performing aggregate calculations.

approx_percentile#

approx_percentile(value, percentile, precision) calculates the approximate value for the given non-negative column and percentile using the HdrHistogram algorithm.

Arguments#

value is any numeric non-negative value.
percentile is a double value between 0.0 and 1.0, inclusive.
precision is an optional int value between 0 and 5, inclusive. This is the number of significant decimal digits to which the histogram will maintain value resolution and separation. For example, when the input column contains integer values between 0 and 3,600,000,000 and the precision is set to 3, value quantization within the range will be no larger than 1/1,000th (or 0.1%) of any value. In this example, the function tracks and analyzes the counts of observed response times ranging between 1 microsecond and 1 hour in magnitude, while maintaining a value resolution of 1 microsecond up to 1 millisecond, a resolution of 1 millisecond (or better) up to one second, and a resolution of 1 second (or better) up to 1,000 seconds. At its maximum tracked value (1 hour), it would still maintain a resolution of 3.6 seconds (or better).

Return value#

Return value type is double.

Examples#

Approximate percentile

SELECT approx_percentile(latency, 0.99) FROM request_logs;

approx_percentile
101.5

avg#

avg(value) calculates simple average of values ignoring missing data (e.g null values).

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

Average transaction amount

SELECT avg(amount) FROM transactions;

avg
22.4

Average transaction amount by payment_type

SELECT payment_type, avg(amount) FROM transactions;

payment_type	avg
cash	22.1
card	27.4
null	18.02

count#

count() or count(*) - counts the number of rows irrespective of underlying data.
count(column_name) - counts the number of non-null values in a given column.

Arguments#

count() does not require arguments.
count(column_name) - supports the following data types:
- double
- float
- integer
- character
- short
- byte
- timestamp
- date
- long
- long256
- geohash
- string
- symbol

Return value#

Return value type is long.

Examples#

Count of rows in the transactions table:

SELECT count() FROM transactions;

count
100

Count of rows in the transactions table aggregated by the payment_type value:

SELECT payment_type, count() FROM transactions;

payment_type	count
cash	25
card	70
null	5

Count non-null transaction amounts:

SELECT count(amount) FROM transactions;

count
95

Count non-null transaction amounts by payment_type:

SELECT payment_type, count(amount) FROM transactions;

payment_type	count
cash	24
card	67
null	4

note

null values are aggregated with count(), but not with count(column_name)

count_distinct#

count_distinct(column_name) - counts distinct values in string, symbol, long256, long, or int columns.

Return value#

Return value type is long.

Examples#

Count of distinct sides in the transactions table. Side column can either be BUY or SELL or null

SELECT count_distinct(side) FROM transactions;

count_distinct
2

Count of distinct counterparties in the transactions table aggregated by payment_type value.

SELECT payment_type, count_distinct(counterparty) FROM transactions;

payment_type	count_distinct
cash	3
card	23
null	5

note

null values are not counted in the count_distinct function.

first/last#

first(column_name) - returns the first value of a column.
last(column_name) - returns the last value of a column.

Supported column datatype: double, float, integer, IPv4, character, short, byte, timestamp, date, long, geohash, symbol, uuid.

If a table has a designated timestamp, then the first row is always the row with the lowest timestamp (oldest) and the last row is always the one with the highest (latest) timestamp. For a table without a designated timestamp column, first returns the first row and last returns the last inserted row, regardless of any timestamp column.

Return value#

Return value type is string.

Examples#

Given a table sensors, which has a designated timestamp column:

device_id	temperature	ts
arduino-01	12	2021-06-02T14:33:19.970258Z
arduino-02	10	2021-06-02T14:33:21.703934Z
arduino-03	18	2021-06-02T14:33:23.707013Z

The following query returns oldest value for the device_id column:

SELECT first(device_id) FROM sensors;

first
arduino-01

The following query returns the latest symbol value for the device_id column:

SELECT last(device_id) FROM sensors;

last
arduino-03

Without selecting a designated timestamp column, the table may be unordered and the query may return different result. Given an unordered table sensors_unordered:

device_id	temperature	ts
arduino-01	12	2021-06-02T14:33:19.970258Z
arduino-03	18	2021-06-02T14:33:23.707013Z
arduino-02	10	2021-06-02T14:33:21.703934Z

The following query returns the first record for the device_id column:

SELECT first(device_id) FROM sensors_unordered;

first
arduino-01

The following query returns the last record for the device_id column:

SELECT last(device_id) FROM sensors_unordered;

last
arduino-02

first_not_null#

first_not_null(column_name) - returns the first non-null value of a column.

Supported column datatype: double, float, integer, IPv4, char, short, byte, timestamp, date, long, geohash, symbol and uuid.

If a table has a designated timestamp, then the first non-null row is always the row with the lowest timestamp (oldest). For a table without a designated timestamp column, first_not_null returns the first non-null row, regardless of any timestamp column.

Return value#

Return value type is string.

Examples#

Given a table sensors, which has a designated timestamp column:

device_id	temperature	ts
null	12	2021-06-02T14:33:19.970258Z
arduino-02	10	2021-06-02T14:33:21.703934Z
arduino-03	18	2021-06-02T14:33:23.707013Z

The following query returns oldest non-null value for the device_id column:

SELECT first_not_null(device_id) FROM sensors;

first_not_null
arduino-02

Without selecting a designated timestamp column, the table may be unordered and the query may return different result. Given an unordered table sensors_unordered:

device_id	temperature	ts
null	12	2021-06-02T14:33:19.970258Z
arduino-03	18	2021-06-02T14:33:23.707013Z
arduino-02	10	2021-06-02T14:33:21.703934Z

The following query returns the first non-null record for the device_id column:

SELECT first_not_null(device_id) FROM sensors_unordered;

first_not_null
arduino-03

last_not_null#

last_not_null(column_name) - returns the last non-null value of a column.

Supported column datatype: double, float, integer, IPv4, char, short, byte, timestamp, date, long, geohash, symbol and uuid.

If a table has a designated timestamp, then the last non-null row is always the row with the highest timestamp (most recent). For a table without a designated timestamp column, last_not_null returns the last non-null row, regardless of any timestamp column.

Return value#

Return value type is string.

Examples#

Given a table sensors, which has a designated timestamp column:

device_id	temperature	ts
null	12	2021-06-02T14:33:19.970258Z
arduino-02	10	2021-06-02T14:33:21.703934Z
arduino-03	18	2021-06-02T14:33:23.707013Z

The following query returns most recent non-null value for the device_id column:

SELECT last_not_null(device_id) FROM sensors;

last_not_null
arduino-03

Without selecting a designated timestamp column, the table may be unordered and the query may return different result. Given an unordered table sensors_unordered:

device_id	temperature	ts
null	12	2021-06-02T14:33:19.970258Z
arduino-03	18	2021-06-02T14:33:23.707013Z
arduino-02	10	2021-06-02T14:33:21.703934Z

The following query returns the last non-null record for the device_id column:

SELECT last_not_null(device_id) FROM sensors_unordered;

last_not_null
arduino-02

haversine_dist_deg#

haversine_dist_deg(lat, lon, ts) - calculates the traveled distance for a series of latitude and longitude points.

Arguments#

lat is the latitude expressed as degrees in decimal format (double)
lon is the longitude expressed as degrees in decimal format (double)
ts is the timestamp for the data point

Return value#

Return value type is double.

Examples#

Calculate the aggregate traveled distance for each car_id

SELECT car_id, haversine_dist_deg(lat, lon, k)
  FROM table rides

ksum#

ksum(value) - adds values ignoring missing data (e.g null values). Values are added using the

Kahan compensated sum algorithm. This is only beneficial for floating-point values such as float or double.

Arguments#

value is any numeric value.

Return value#

Return value type is the same as the type of the argument.

Examples#

SELECT ksum(a)
FROM (SELECT rnd_double() a FROM long_sequence(100));

ksum
52.79143968514029

max#

max(value) - returns the highest value ignoring missing data (e.g null values).

Arguments#

value is any numeric or string value

Return value#

Return value type is the same as the type of the argument.

Examples#

Highest transaction amount

SELECT max(amount) FROM transactions;

max
55.3

Highest transaction amount by payment_type

SELECT payment_type, max(amount) FROM transactions;

payment_type	amount
cash	31.5
card	55.3
null	29.2

min#

min(value) - returns the lowest value ignoring missing data (e.g null values).

Arguments#

value is any numeric or string value

Return value#

Return value type is the same as the type of the argument.

Examples#

Lowest transaction amount

SELECT min(amount) FROM transactions;

min
12.5

Lowest transaction amount, by payment_type

SELECT payment_type, min(amount) FROM transactions;

payment_type	min
cash	12.5
card	15.3
null	22.2

nsum#

nsum(value) - adds values ignoring missing data (e.g null values). Values are added using the Neumaier sum algorithm. This is only beneficial for floating-point values such as float or double.

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

SELECT nsum(a)
FROM (SELECT rnd_double() a FROM long_sequence(100));

nsum
49.5442334742831

stddev / stddev_samp#

stddev_samp(value) - Calculates the sample standard deviation of a set of values, ignoring missing data (e.g., null values). The sample standard deviation is a measure of the amount of variation or dispersion in a sample of a population. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

stddev is an alias for stddev_samp.

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

SELECT stddev_samp(x)
FROM (SELECT x FROM long_sequence(100));

stddev_samp
29.011491975882

stddev_pop#

stddev_pop(value) - Calculates the population standard deviation of a set of values. The population standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

SELECT stddev_pop(x)
FROM (SELECT x FROM long_sequence(100));

stddev_samp
28.86607004772212

string_agg#

string_agg(value, delimiter) - Concatenates the given string values into a single string with the delimiter used as a value separator.

Arguments#

value is a string value.
delimiter is a char value.

Return value#

Return value type is string.

Examples#

SELECT string_agg(x::string, ',')
FROM (SELECT x FROM long_sequence(5));

string_agg
1,2,3,4,5

sum#

sum(value) - adds values ignoring missing data (e.g null values).

Arguments#

value is any numeric value.

Return value#

Return value type is the same as the type of the argument.

Examples#

Sum all quantities in the transactions table

SELECT sum(quantity) FROM transactions;

sum
100

Sum all quantities in the transactions table, aggregated by item

SELECT item, sum(quantity) FROM transactions;

item	count
apple	53
orange	47

Overflow#

sum does not perform overflow check. To avoid overflow, you can cast the argument to wider type.

Cast as long to avoid overflow

SELECT sum(cast(a AS LONG)) FROM table;

variance / var_samp#

var_samp(value) - Calculates the sample variance of a set of values. The sample variance is a measure of the amount of variation or dispersion of a set of values in a sample from a population. A low variance indicates that the values tend to be very close to the mean, while a high variance indicates that the values are spread out over a wider range.

variance() is an alias for var_samp.

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

SELECT var_samp(x)
FROM (SELECT x FROM long_sequence(100));

stddev_samp
841.666666666666

var_pop#

var_pop(value) - Calculates the population variance of a set of values. The population variance is a measure of the amount of variation or dispersion of a set of values. A low variance indicates that the values tend to be very close to the mean, while a high variance indicates that the values are spread out over a wider range.

Arguments#

value is any numeric value.

Return value#

Return value type is double.

Examples#

SELECT var_pop(x)
FROM (SELECT x FROM long_sequence(100));

stddev_samp
833.25

vwap#

vwap(price, quantity) - Calculates the volume-weighted average price (VWAP) based on the given price and quantity columns. This is a handy replacement for the sum(price * quantity) / sum(quantity) expression.

Arguments#

price is any numeric price value.
quantity is any numeric quantity value.

Return value#

Return value type is double.

Examples#

SELECT vwap(x, x)
FROM (SELECT x FROM long_sequence(100));

vwap
67