Aggregation

When selecting data from CrateDB, you can use an aggregate function to calculate a single summary value for one or more columns.

For example:

cr> SELECT count(*) FROM locations;
+----------+
| count(*) |
+----------+
|       13 |
+----------+
SELECT 1 row in set (... sec)

Here, the count(*) function computes the result across all rows.

Aggregate functions can be used with the GROUP BY clause. When used like this, an aggregate function returns a single summary value for each grouped collection of column values.

For example:

cr> SELECT kind, count(*) FROM locations GROUP BY kind;
+-------------+----------+
| kind        | count(*) |
+-------------+----------+
| Galaxy      |        4 |
| Star System |        4 |
| Planet      |        5 |
+-------------+----------+
SELECT 3 rows in set (... sec)

Tip

Aggregation works across all the rows that match a query or on all matching rows in every distinct group of a GROUP BY statement. Aggregating SELECT statements without GROUP BY will always return one row.

Table of contents

Aggregate expressions

An aggregate expression represents the application of an aggregate function across rows selected by a query. Besides the function signature, expressions might contain supplementary clauses and keywords.

The synopsis of an aggregate expression is one of the following:

aggregate_function ( * ) [ FILTER ( WHERE condition ) ]
aggregate_function ( [ DISTINCT ] expression [ , ... ] ) [ FILTER ( WHERE condition ) ]

Here, aggregate_function is a name of an aggregate function and expression is a column reference, scalar function or literal.

If FILTER is specified, then only the rows that met the WHERE clause condition are supplied to the aggregate function.

The optional DISTINCT keyword is only supported by aggregate functions that explicitly mention its support. Please refer to existing limitations for further information.

The aggregate expression form that uses a wildcard instead of an expression as a function argument is supported only by the count(*) aggregate function.

Aggregate functions

arbitrary(column)

The arbitrary aggregate function returns a single value of a column. Which value it returns is not defined.

Its return type is the type of its parameter column and can be NULL if the column contains NULL values.

Example:

cr> select arbitrary(position) from locations;
+---------------------+
| arbitrary(position) |
+---------------------+
| ...                 |
+---------------------+
SELECT 1 row in set (... sec)

cr> select arbitrary(name), kind from locations
... where name != ''
... group by kind order by kind desc;
+-...-------------+-------------+
| arbitrary(name) | kind        |
+-...-------------+-------------+
| ...             | Star System |
| ...             | Planet      |
| ...             | Galaxy      |
+-...-------------+-------------+
SELECT 3 rows in set (... sec)

An example use case is to group a table with many rows per user by user_id and get the username for every group, that means every user. This works as rows with same user_id have the same username. This method performs better than grouping on username as grouping on number types is generally faster than on strings. The advantage is that the arbitrary function does very little to no computation as for example max aggregate function would do.

any_value(column)

any_value is an alias for arbitrary.

Example:

cr> select any_value(x) from unnest([1, 1]) t (x);
+--------------+
| any_value(x) |
+--------------+
| 1            |
+--------------+
SELECT 1 row in set (... sec)

array_agg(column)

The array_agg aggregate function concatenates all input values into an array.

cr> SELECT array_agg(x) FROM (VALUES (42), (832), (null), (17)) as t (x);
+---------------------+
| array_agg(x)        |
+---------------------+
| [42, 832, null, 17] |
+---------------------+
SELECT 1 row in set (... sec)

See also

string_agg(column, delimiter)

avg(column)

The avg and mean aggregate function returns the arithmetic mean, the average, of all values in a column that are not NULL. It accepts all numeric, timestamp and interval types as single argument. For numeric argument type the return type is numeric, for interval argument type the return type is interval and for other argument type the return type is double.

Example:

cr> select avg(position), kind from locations
... group by kind order by kind;
+---------------+-------------+
| avg(position) | kind        |
+---------------+-------------+
| 3.25          | Galaxy      |
| 3.0           | Planet      |
| 2.5           | Star System |
+---------------+-------------+
SELECT 3 rows in set (... sec)

The avg aggregation on the bigint column might result in a precision error if sum of elements exceeds 2^53:

cr> select avg(t.val) from
... (select unnest([9223372036854775807, 9223372036854775807]) as val) t;
+-----------------------+
|              avg(val) |
+-----------------------+
| 9.223372036854776e+18 |
+-----------------------+
SELECT 1 row in set (... sec)

To address the precision error of the avg aggregation, we cast the aggregation column to the numeric data type:

cr> select avg(t.val :: numeric) from
... (select unnest([9223372036854775807, 9223372036854775807]) as val) t;
+---------------------------+
| avg(cast(val AS NUMERIC)) |
+---------------------------+
|       9223372036854775807 |
+---------------------------+
SELECT 1 row in set (... sec)

avg(DISTINCT column)

The avg aggregate function also supports the distinct keyword. This keyword changes the behaviour of the function so that it will only average the number of distinct values in this column that are not NULL:

cr> select
...   avg(distinct position) AS avg_pos,
...   count(*),
...   date
... from locations group by date
... order by 1 desc, count(*) desc;
+---------+----------+---------------+
| avg_pos | count(*) |          date |
+---------+----------+---------------+
|     4.0 |        1 | 1367366400000 |
|     3.6 |        8 | 1373932800000 |
|     2.0 |        4 |  308534400000 |
+---------+----------+---------------+
SELECT 3 rows in set (... sec)

cr> select avg(distinct position) AS avg_pos from locations;
+---------+
| avg_pos |
+---------+
|     3.5 |
+---------+
SELECT 1 row in set (... sec)

count(column)

In contrast to the count(*) function the count function used with a column name as parameter will return the number of rows with a non-NULL value in that column.

Example:

cr> select count(name), count(*), date from locations group by date
... order by count(name) desc, count(*) desc;
+-------------+----------+---------------+
| count(name) | count(*) | date          |
+-------------+----------+---------------+
| 7           | 8        | 1373932800000 |
| 4           | 4        | 308534400000  |
| 1           | 1        | 1367366400000 |
+-------------+----------+---------------+
SELECT 3 rows in set (... sec)

count(DISTINCT column)

The count aggregate function also supports the distinct keyword. This keyword changes the behaviour of the function so that it will only count the number of distinct values in this column that are not NULL:

cr> select
...   count(distinct kind) AS num_kind,
...   count(*),
...   date
... from locations group by date
... order by num_kind, count(*) desc;
+----------+----------+---------------+
| num_kind | count(*) |          date |
+----------+----------+---------------+
|        1 |        1 | 1367366400000 |
|        3 |        8 | 1373932800000 |
|        3 |        4 |  308534400000 |
+----------+----------+---------------+
SELECT 3 rows in set (... sec)

cr> select count(distinct kind) AS num_kind from locations;
+----------+
| num_kind |
+----------+
|        3 |
+----------+
SELECT 1 row in set (... sec)

See also

hyperloglog_distinct(column, [precision]) for an alternative that trades some accuracy for improved performance.

count(*)

This aggregate function simply returns the number of rows that match the query.

count(columName) is also possible, but currently only works on a primary key column. The semantics are the same.

The return value is always of type bigint.

cr> select count(*) from locations;
+----------+
| count(*) |
+----------+
| 13       |
+----------+
SELECT 1 row in set (... sec)

count(*) can also be used on group by queries:

cr> select count(*), kind from locations group by kind order by kind asc;
+----------+-------------+
| count(*) | kind        |
+----------+-------------+
| 4        | Galaxy      |
| 5        | Planet      |
| 4        | Star System |
+----------+-------------+
SELECT 3 rows in set (... sec)

geometric_mean(column)

The geometric_mean aggregate function computes the geometric mean, a mean for positive numbers. For details see: Geometric Mean.

geometric mean is defined on all numeric types and on timestamp. It always returns double values. If a value is negative, all values were null or we got no value at all NULL is returned. If any of the aggregated values is 0 the result will be 0.0 as well.

Caution

Due to java double precision arithmetic it is possible that any two executions of the aggregate function on the same data produce slightly differing results.

Example:

cr> select geometric_mean(position), kind from locations
... group by kind order by kind;
+--------------------------+-------------+
| geometric_mean(position) | kind        |
+--------------------------+-------------+
|       2.6321480259049848 | Galaxy      |
|       2.6051710846973517 | Planet      |
|       2.213363839400643  | Star System |
+--------------------------+-------------+
SELECT 3 rows in set (... sec)

hyperloglog_distinct(column, [precision])

The hyperloglog_distinct aggregate function calculates an approximate count of distinct non-null values using the HyperLogLog++ algorithm.

The return value data type is always a bigint.

The first argument can be a reference to a column of all Primitive types. Container types and Geographic types are not supported.

The optional second argument defines the used precision for the HyperLogLog++ algorithm. This allows to trade memory for accuracy, valid values are 4 to 18. A precision of 4 uses approximately 16 bytes of memory. Each increase in precision doubles the memory requirement. So precision 5 uses approximately 32 bytes, up to 262144 bytes for precision 18.

The default value for the precision which is used if the second argument is left out is 14.

Examples:

cr> select hyperloglog_distinct(position) from locations;
+--------------------------------+
| hyperloglog_distinct(position) |
+--------------------------------+
| 6                              |
+--------------------------------+
SELECT 1 row in set (... sec)

cr> select hyperloglog_distinct(position, 4) from locations;
+-----------------------------------+
| hyperloglog_distinct(position, 4) |
+-----------------------------------+
| 6                                 |
+-----------------------------------+
SELECT 1 row in set (... sec)

mean(column)

An alias for avg(column).

min(column)

The min aggregate function returns the smallest value in a column that is not NULL. Its single argument is a column name and its return value is always of the type of that column.

Example:

cr> select min(position), kind
... from locations
... where name not like 'North %'
... group by kind order by min(position) asc, kind asc;
+---------------+-------------+
| min(position) | kind        |
+---------------+-------------+
| 1             | Planet      |
| 1             | Star System |
| 2             | Galaxy      |
+---------------+-------------+
SELECT 3 rows in set (... sec)

cr> select min(date) from locations;
+--------------+
| min(date)    |
+--------------+
| 308534400000 |
+--------------+
SELECT 1 row in set (... sec)

min returns NULL if the column does not contain any value but NULL. It is allowed on columns with primitive data types. On text columns it will return the lexicographically smallest.

cr> select min(name), kind from locations
... group by kind order by kind asc;
+------------------------------------+-------------+
| min(name)                          | kind        |
+------------------------------------+-------------+
| Galactic Sector QQ7 Active J Gamma | Galaxy      |
|                                    | Planet      |
| Aldebaran                          | Star System |
+------------------------------------+-------------+
SELECT 3 rows in set (... sec)

max(column)

It behaves exactly like min but returns the biggest value in a column that is not NULL.

Some Examples:

cr> select max(position), kind from locations
... group by kind order by kind desc;
+---------------+-------------+
| max(position) | kind        |
+---------------+-------------+
| 4             | Star System |
| 5             | Planet      |
| 6             | Galaxy      |
+---------------+-------------+
SELECT 3 rows in set (... sec)

cr> select max(position) from locations;
+---------------+
| max(position) |
+---------------+
| 6             |
+---------------+
SELECT 1 row in set (... sec)

cr> select max(name), kind from locations
... group by kind order by max(name) desc;
+-------------------+-------------+
| max(name)         | kind        |
+-------------------+-------------+
| Outer Eastern Rim | Galaxy      |
| Bartledan         | Planet      |
| Altair            | Star System |
+-------------------+-------------+
SELECT 3 rows in set (... sec)

max_by(returnField, searchField)

Returns the value of returnField where searchField has the highest value.

If there are ties for searchField the result is non-deterministic and can be any of the returnField values of the ties.

NULL values in the searchField don’t count as max but are skipped.

An Example:

cr> SELECT max_by(mountain, height) FROM sys.summits;
+--------------------------+
| max_by(mountain, height) |
+--------------------------+
| Mont Blanc               |
+--------------------------+
SELECT 1 row in set (... sec)

min_by(returnField, searchField)

Returns the value of returnField where searchField has the lowest value.

If there are ties for searchField the result is non-deterministic and can be any of the returnField values of the ties.

NULL values in the searchField don’t count as min but are skipped.

An Example:

cr> SELECT min_by(mountain, height) FROM sys.summits;
+--------------------------+
| min_by(mountain, height) |
+--------------------------+
| Puy de Rent              |
+--------------------------+
SELECT 1 row in set (... sec)

stddev(column)

The stddev aggregate function computes the Standard Deviation of the set of non-null values in a column. It is a measure of the variation of data values. A low standard deviation indicates that the values tend to be near the mean.

stddev is defined on all numeric types and on timestamp. It always returns double precision values. If all values were null or we got no value at all NULL is returned.

Example:

cr> select stddev(position), kind from locations
... group by kind order by kind;
+--------------------+-------------+
|   stddev(position) | kind        |
+--------------------+-------------+
| 1.920286436967152  | Galaxy      |
| 1.4142135623730951 | Planet      |
| 1.118033988749895  | Star System |
+--------------------+-------------+
SELECT 3 rows in set (... sec)

Caution

Due to java double precision arithmetic it is possible that any two executions of the aggregate function on the same data produce slightly differing results.

string_agg(column, delimiter)

The string_agg aggregate function concatenates the input values into a string, where each value is separated by a delimiter.

If all input values are null, null is returned as a result.

cr> select string_agg(col1, ', ') from (values('a'), ('b'), ('c')) as t;
+------------------------+
| string_agg(col1, ', ') |
+------------------------+
| a, b, c                |
+------------------------+
SELECT 1 row in set (... sec)

See also

array_agg(column)

percentile(column, {fraction | fractions})

The percentile aggregate function computes a Percentile over numeric non-null values in a column.

Percentiles show the point at which a certain percentage of observed values occur. For example, the 98th percentile is the value which is greater than 98% of the observed values. The result is defined and computed as an interpolated weighted average. According to that it allows the median of the input data to be defined conveniently as the 50th percentile.

The function expects a single fraction or an array of fractions and a column name. Independent of the input column data type the result of percentile always returns a double precision. If the value at the specified column is null the row is ignored. Fractions must be double precision values between 0 and 1. When supplied a single fraction, the function will return a single value corresponding to the percentile of the specified fraction:

cr> select percentile(position, 0.95), kind from locations
... group by kind order by kind;
+----------------------------+-------------+
| percentile(position, 0.95) | kind        |
+----------------------------+-------------+
|                        6.0 | Galaxy      |
|                        5.0 | Planet      |
|                        4.0 | Star System |
+----------------------------+-------------+
SELECT 3 rows in set (... sec)

When supplied an array of fractions, the function will return an array of values corresponding to the percentile of each fraction specified:

cr> select percentile(position, [0.0013, 0.9987]) as perc from locations;
+------------+
| perc       |
+------------+
| [1.0, 6.0] |
+------------+
SELECT 1 row in set (... sec)

When a query with percentile function won’t match any rows then a null result is returned.

To be able to calculate percentiles over a huge amount of data and to scale out CrateDB calculates approximate instead of accurate percentiles. The algorithm used by the percentile metric is called TDigest. The accuracy/size trade-off of the algorithm is defined by a single compression parameter which has a constant value of 100. However, there are a few guidelines to keep in mind in this implementation:

  • Extreme percentiles (e.g. 99%) are more accurate.

  • For small sets, percentiles are highly accurate.

  • It is difficult to generalize the exact level of accuracy, as it depends on your data distribution and volume of data being aggregated.

sum(column)

Returns the sum of a set of numeric input values that are not NULL. Depending on the argument type a suitable return type is chosen. For interval argument types the return type is interval. For real and double precision argument types the return type is equal to the argument type. For byte, smallint, integer and bigint the return type changes to bigint. If the range of bigint values (-2^64 to 2^64-1) gets exceeded an ArithmeticException will be raised.

cr> select sum(position), kind from locations
... group by kind order by sum(position) asc;
+---------------+-------------+
| sum(position) | kind        |
+---------------+-------------+
| 10            | Star System |
| 13            | Galaxy      |
| 15            | Planet      |
+---------------+-------------+
SELECT 3 rows in set (... sec)

cr> select sum(position) as position_sum from locations;
+--------------+
| position_sum |
+--------------+
| 38           |
+--------------+
SELECT 1 row in set (... sec)

cr> select sum(name), kind from locations group by kind order by sum(name) desc;
SQLParseException[Cannot cast value `Aldebaran` to type `byte`]

If the sum aggregation on a numeric data type with the fixed length can potentially exceed its range it is possible to handle the overflow by casting the function argument to the numeric type with an arbitrary precision.

The sum aggregation on the bigint column will result in an overflow in the following aggregation query:

cr> SELECT sum(count)
... FROM uservisits;
ArithmeticException[long overflow]

To address the overflow of the sum aggregation on the given field, we cast the aggregation column to the numeric data type:

cr> SELECT sum(count::numeric)
... FROM uservisits;
+-----------------------------+
| sum(cast(count AS NUMERIC)) |
+-----------------------------+
|         9223372036854775816 |
+-----------------------------+
SELECT 1 row in set (... sec)

variance(column)

The variance aggregate function computes the Variance of the set of non-null values in a column. It is a measure about how far a set of numbers is spread. A variance of 0.0 indicates that all values are the same.

variance is defined on all numeric types and on timestamp. It returns a double precision value. If all values were null or we got no value at all NULL is returned.

Example:

cr> select variance(position), kind from locations
... group by kind order by kind desc;
+--------------------+-------------+
| variance(position) | kind        |
+--------------------+-------------+
|             1.25   | Star System |
|             2.0    | Planet      |
|             3.6875 | Galaxy      |
+--------------------+-------------+
SELECT 3 rows in set (... sec)

Caution

Due to java double precision arithmetic it is possible that any two executions of the aggregate function on the same data produce slightly differing results.

topk(column, [k], [max_capacity])

The topk aggregate function computes k most frequent values. The result is an OBJECT in the following format:

{
    "frequencies": [
        {
            "estimate": <estimated_frequency>,
            "item": <value_of_column>,
            "lower_bound": <lower_bound>,
            "upper_bound": <upper_bound>"
        },
        ...
    ],
    "maximum_error": <max_error>
}

The frequencies list is ordered by the estimated frequency, with the most common items listed first.

k defaults to 8 and can’t exceed 5000. The max_capacity parameter is optional and describes the maximum number of tracked items and must be in the power of 2 and defaults to 8192.

Example:

cr> select topk(country, 3) from sys.summits;
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| topk(country, 3)                                                                                                                                                                                                                                                 |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| {"frequencies": [{"estimate": 436, "item": "IT", "lower_bound": 436, "upper_bound": 436}, {"estimate": 401, "item": "AT", "lower_bound": 401, "upper_bound": 401}, {"estimate": 320, "item": "CH", "lower_bound": 320, "upper_bound": 320}], "maximum_error": 0} |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
SELECT 1 row in set (... sec)

Internally a Frequency Sketch is used to track the frequencies of the most common values. Higher values in max_capacity provide better accuracy at the cost of increased memory usage. If less different items than 75 % of the max_capacity are processed the frequencies of the result are exact, otherwise they will be an approximation. The result contains all values with their frequencies above the error threshold and may also include false positives. The error threshold indicates the minimum frequency which can be detected reliably and is defined as followed:

M = max_capacity, always a power of 2
N = Total counts of items
e = Epsilon = 3.5/M (minimum detectable frequency)

error threshold = (N < 0.75 * M)? 0 : e * N.

The following table is an extract of the Error Threshold Table and shows the error threshold in relation to the max_capacity and the number of processed items. A threshold of 0 indicates that the frequencies are exact.

Error Threshold

max_capacity vs. items

8192

16384

32768

65536

131072

262144

524288

10000

4

0

0

0

0

0

0

100000

43

21

11

5

3

0

0

1000000

427

214

107

53

27

13

7

10000000

4272

2136

1068

534

267

134

67

100000000

42725

21362

10681

5341

2670

1335

668

1000000000

427246

213623

106812

53406

26703

13351

6676

The error threshold shows which ranges of frequencies can be tracked depending on the number of items and capacity. E.g. Processing 10,000 items with the max_capacity of 8192 indicates a error threshold of 4. Therefore all items with frequencies greater 4 will be included. Some items with frequencies below the threshold 4 may also appear in the result.

Limitations

  • DISTINCT is not supported with aggregations on Joins.

  • Aggregate functions can only be applied to columns with a plain index, which is the default for all primitive type columns.