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SQL | Expressions | Comparison Operators | <div id="rrdiagram2"></div>
The table below shows the standard comparison operators.
Whenever either of the input arguments is `NULL`, the output of the comparison is `NULL`.
<div class="narrow_table"></div>
| Operator | Description | Example | Result |
|:---|:---|:---|:---|
| `<` | less than | `2 < 3` | `true` |
| `>` | greater than | `2 > 3` | `false` |
| `<=` | less than or equal to | `2 <= 3` | `true` |
| `>=` | greater than or equal to | `4 >= NULL` | `NULL` |
| `=` | equal | `NULL = NULL` | `NULL` |
| `<>` or `!=` | not equal | `2 <> 2` | `false` |
The table below shows the standard distinction operators.
These operators treat `NULL` values as equal.
<div class="narrow_table"></div>
| Operator | Description | Example | Result |
|:---|:---|:---|:-|
| `IS DISTINCT FROM` | not equal, including `NULL` | `2 IS DISTINCT FROM NULL` | `true` |
| `IS NOT DISTINCT FROM` | equal, including `NULL` | `NULL IS NOT DISTINCT FROM NULL` | `true` | | 297 | [
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|
SQL | Expressions | `BETWEEN` and `IS [NOT] NULL` | <div id="rrdiagram1"></div>
Besides the standard comparison operators there are also the `BETWEEN` and `IS (NOT) NULL` operators. These behave much like operators, but have special syntax mandated by the SQL standard. They are shown in the table below.
Note that `BETWEEN` and `NOT BETWEEN` are only equivalent to the examples below in the cases where both `a`, `x` and `y` are of the same type, as `BETWEEN` will cast all of its inputs to the same type.
<div class="narrow_table"></div>
| Predicate | Description |
|:---|:---|
| `a BETWEEN x AND y` | equivalent to `x <= a AND a <= y` |
| `a NOT BETWEEN x AND y` | equivalent to `x > a OR a > y` |
| `expression IS NULL` | `true` if expression is `NULL`, `false` otherwise |
| `expression ISNULL` | alias for `IS NULL` (non-standard) |
| `expression IS NOT NULL` | `false` if expression is `NULL`, `true` otherwise |
| `expression NOTNULL` | alias for `IS NOT NULL` (non-standard) |
> For the expression `BETWEEN x AND y`, `x` is used as the lower bound and `y` is used as the upper bound. Therefore, if `x > y`, the result will always be `false`. | 312 | [
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|
SQL | Expressions | `IN` | The `IN` operator checks containment of the left expression inside the set of expressions on the right hand side (RHS). The `IN` operator returns true if the expression is present in the RHS, false if the expression is not in the RHS and the RHS has no `NULL` values, or `NULL` if the expression is not in the RHS and the RHS has `NULL` values.
```sql
SELECT 'Math' IN ('CS', 'Math');
```
```text
true
```
```sql
SELECT 'English' IN ('CS', 'Math');
```
```text
false
```
```sql
SELECT 'Math' IN ('CS', 'Math', NULL);
```
```text
true
```
```sql
SELECT 'English' IN ('CS', 'Math', NULL);
```
```text
NULL
``` | 177 | [
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] |
|
SQL | Expressions | `NOT IN` | `NOT IN` can be used to check if an element is not present in the set. `x NOT IN y` is equivalent to `NOT (x IN y)`. | 36 | [
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|
SQL | Expressions | Use with Subqueries | The `IN` operator can also be used with a subquery that returns a single column. See the [subqueries page for more information](#docs:sql:expressions:subqueries). | 40 | [
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|
SQL | Expressions | Logical Operators | <div id="rrdiagram"></div>
The following logical operators are available: `AND`, `OR` and `NOT`. SQL uses a three-valuad logic system with `true`, `false` and `NULL`. Note that logical operators involving `NULL` do not always evaluate to `NULL`. For example, `NULL AND false` will evaluate to `false`, and `NULL OR true` will evaluate to `true`. Below are the complete truth tables.
#### Binary Operators: `AND` and `OR` {#docs:sql:expressions:logical_operators::binary-operators-and-and-or}
<div class="narrow_table"></div>
| `a` | `b` | `a AND b` | `a OR b` |
|:---|:---|:---|:---|
| `true` | `true` | `true` | `true` |
| `true` | `false` | `false` | `true` |
| `true` | `NULL` | `NULL` | `true` |
| `false` | `false` | `false` | `false` |
| `false` | `NULL` | `false` | `NULL` |
| `NULL` | `NULL` | `NULL` | `NULL`|
#### Unary Operator: `NOT` {#docs:sql:expressions:logical_operators::unary-operator-not}
<div class="narrow_table"></div>
| `a` | `NOT a` |
|:---|:---|
| `true` | `false` |
| `false` | `true` |
| `NULL` | `NULL` |
The operators `AND` and `OR` are commutative, that is, you can switch the left and right operand without affecting the result. | 386 | [
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|
SQL | Expressions | Examples | Select all columns present in the `FROM` clause:
```sql
SELECT * FROM table_name;
```
Count the number of rows in a table:
```sql
SELECT count(*) FROM table_name;
```
DuckDB offers a shorthand for `count(*)` expressions where the `*` may be omitted:
```sql
SELECT count() FROM table_name;
```
Select all columns from the table called `table_name`:
```sql
SELECT table_name.*
FROM table_name
JOIN other_table_name USING (id);
```
Select all columns except the city column from the addresses table:
```sql
SELECT * EXCLUDE (city)
FROM addresses;
```
Select all columns from the addresses table, but replace city with `lower(city)`:
```sql
SELECT * REPLACE (lower(city) AS city)
FROM addresses;
```
Select all columns matching the given expression:
```sql
SELECT COLUMNS(c -> c LIKE '%num%')
FROM addresses;
```
Select all columns matching the given regex from the table:
```sql
SELECT COLUMNS('number\d+')
FROM addresses;
``` | 231 | [
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SQL | Expressions | Syntax | <div id="rrdiagram"></div> | 9 | [
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|
SQL | Expressions | Star Expression | The `*` expression can be used in a `SELECT` statement to select all columns that are projected in the `FROM` clause.
```sql
SELECT *
FROM tbl;
```
The `*` expression can be modified using the `EXCLUDE` and `REPLACE`.
#### `EXCLUDE` Clause {#docs:sql:expressions:star::exclude-clause}
`EXCLUDE` allows us to exclude specific columns from the `*` expression.
```sql
SELECT * EXCLUDE (col)
FROM tbl;
```
#### `REPLACE` Clause {#docs:sql:expressions:star::replace-clause}
`REPLACE` allows us to replace specific values in columns as specified by an expression.
```sql
SELECT * REPLACE (col / 1_000 AS col)
FROM tbl;
``` | 175 | [
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|
SQL | Expressions | `COLUMNS` Expression | The `COLUMNS` expression can be used to execute the same expression on the values in multiple columns. For example:
```sql
CREATE TABLE numbers (id INTEGER, number INTEGER);
INSERT INTO numbers VALUES (1, 10), (2, 20), (3, NULL);
SELECT min(COLUMNS(*)), count(COLUMNS(*)) FROM numbers;
```
<div class="narrow_table"></div>
| id | number | id | number |
|---:|-------:|---:|-------:|
| 1 | 10 | 3 | 2 |
The `*` expression in the `COLUMNS` statement can also contain `EXCLUDE` or `REPLACE`, similar to regular star expressions.
```sql
SELECT
min(COLUMNS(* REPLACE (number + id AS number))),
count(COLUMNS(* EXCLUDE (number)))
FROM numbers;
```
<div class="narrow_table"></div>
| id | min(number := (number + id)) | id |
|---:|-----------------------------:|---:|
| 1 | 11 | 3 |
`COLUMNS` expressions can also be combined, as long as the `COLUMNS` contains the same (star) expression:
```sql
SELECT COLUMNS(*) + COLUMNS(*) FROM numbers;
```
<div class="narrow_table"></div>
| id | number |
|---:|-------:|
| 2 | 20 |
| 4 | 40 |
| 6 | NULL |
`COLUMNS` expressions can also be used in `WHERE` clauses. The conditions are applied to all columns and are combined using the logical `AND` operator.
```sql
SELECT *
FROM (
SELECT 0 AS x, 1 AS y, 2 AS z
UNION ALL
SELECT 1 AS x, 2 AS y, 3 AS z
UNION ALL
SELECT 2 AS x, 3 AS y, 4 AS z
)
WHERE COLUMNS(*) > 1; -- equivalent to: x > 1 AND y > 1 AND z > 1
```
<div class="narrow_table"></div>
| x | y | z |
|--:|--:|--:|
| 2 | 3 | 4 | | 489 | [
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|
SQL | Expressions | `COLUMNS` Regular Expression | `COLUMNS` supports passing a regex in as a string constant:
```sql
SELECT COLUMNS('(id|numbers?)') FROM numbers;
```
<div class="narrow_table"></div>
| id | number |
|---:|-------:|
| 1 | 10 |
| 2 | 20 |
| 3 | NULL |
#### Renaming Columns Using a `COLUMNS` Expression {#docs:sql:expressions:star::renaming-columns-using-a-columns-expression}
The matches of capture groups can be used to rename columns selected by a regular expression.
The capture groups are one-indexed; `\0` is the original column name.
For example, to select the first three letters of colum names, run:
```sql
SELECT COLUMNS('(\w{3}).*') AS '\1' FROM numbers;
```
<div class="narrow_table"></div>
| id | num |
|---:|-----:|
| 1 | 10 |
| 2 | 20 |
| 3 | NULL |
To remove a colon (` :`) character in the middle of a column name, run:
```sql
CREATE TABLE tbl ("Foo:Bar" INTEGER, "Foo:Baz" INTEGER, "Foo:Qux" INTEGER);
SELECT COLUMNS('(\w*):(\w*)') AS '\1\2' FROM tbl;
``` | 304 | [
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-0.037383195012807846,
0.033202219754457474,
-0.007089233957231045,
-0.035599347203969955,
-0.008142001926898956
] |
|
SQL | Expressions | `COLUMNS` Lambda Function | `COLUMNS` also supports passing in a lambda function. The lambda function will be evaluated for all columns present in the `FROM` clause, and only columns that match the lambda function will be returned. This allows the execution of arbitrary expressions in order to select and rename columns.
```sql
SELECT COLUMNS(c -> c LIKE '%num%') FROM numbers;
```
<div class="narrow_table"></div>
| number |
|-------:|
| 10 |
| 20 |
| NULL | | 107 | [
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|
SQL | Expressions | `*COLUMNS` Unpacked Columns | The `*COLUMNS` clause is a variation of `COLUMNS`, which supports all of the previously mentioned capabilities.
The difference is in how the expression expands.
`*COLUMNS` will expand in-place, much like the [iterable unpacking behavior in Python](https://peps.python.org/pep-3132/), which inspired the `*` syntax.
This implies that the expression expands into the parent expression.
An example that shows this difference between `COLUMNS` and `*COLUMNS`:
With `COLUMNS`:
```sql
SELECT coalesce(COLUMNS(['a', 'b', 'c'])) AS result
FROM (SELECT NULL a, 42 b, true c);
```
| result | result | result |
|--------|-------:|-------:|
| NULL | 42 | true |
With `*COLUMNS`, the expression expands in its parent expression `coalesce`, resulting in a single result column:
```sql
SELECT coalesce(*COLUMNS(['a', 'b', 'c'])) AS result
FROM (SELECT NULL AS a, 42 AS b, true AS c);
```
| result |
|-------:|
| 42 |
`*COLUMNS` also works with the `(*)` argument:
```sql
SELECT coalesce(*COLUMNS(*)) AS result
FROM (SELECT NULL a, 42 AS b, true AS c);
```
| result |
|-------:|
| 42 | | 311 | [
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|
SQL | Expressions | `STRUCT.*` | The `*` expression can also be used to retrieve all keys from a struct as separate columns.
This is particularly useful when a prior operation creates a struct of unknown shape, or if a query must handle any potential struct keys.
See the [`STRUCT` data type](#docs:sql:data_types:struct) and [`STRUCT` functions](#docs:sql:functions:struct) pages for more details on working with structs.
For example:
```sql
SELECT st.* FROM (SELECT {'x': 1, 'y': 2, 'z': 3} AS st);
```
<div class="narrow_table"></div>
| x | y | z |
|--:|--:|--:|
| 1 | 2 | 3 | | 158 | [
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|
SQL | Expressions | Subqueries | Subqueries are parenthesized query expressions that appear as part of a larger, outer query. Subqueries are usually based on `SELECT ... FROM`, but in DuckDB other query constructs such as [`PIVOT`](#docs:sql:statements:pivot) can also appear as a subquery. | 60 | [
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|
SQL | Expressions | Scalar Subquery | <div id="rrdiagram1"></div>
Scalar subqueries are subqueries that return a single value. They can be used anywhere where an expression can be used. If a scalar subquery returns more than a single value, an error is raised (unless `scalar_subquery_error_on_multiple_rows` is set to `false`, in which case a row is selected randomly).
Consider the following table:
#### Grades {#docs:sql:expressions:subqueries::grades}
<div class="narrow_table"></div>
| grade | course |
|---:|:---|
| 7 | Math |
| 9 | Math |
| 8 | CS |
```sql
CREATE TABLE grades (grade INTEGER, course VARCHAR);
INSERT INTO grades VALUES (7, 'Math'), (9, 'Math'), (8, 'CS');
```
We can run the following query to obtain the minimum grade:
```sql
SELECT min(grade) FROM grades;
```
| min(grade) |
|-----------:|
| 7 |
By using a scalar subquery in the `WHERE` clause, we can figure out for which course this grade was obtained:
```sql
SELECT course FROM grades WHERE grade = (SELECT min(grade) FROM grades);
```
| course |
|--------|
| Math | | 278 | [
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|
SQL | Expressions | Subquery Comparisons: `ALL`, `ANY` and `SOME` | In the section on [scalar subqueries](#::scalar-subquery), a scalar expression was compared directly to a subquery using the equality [comparison operator](#docs:sql:expressions:comparison_operators::comparison-operators) (` =`).
Such direct comparisons only make sense with scalar subqueries.
Scalar expressions can still be compared to single-column subqueries returning multiple rows by specifying a quantifier. Available quantifiers are `ALL`, `ANY` and `SOME`. The quantifiers `ANY` and `SOME` are equivalent.
#### `ALL` {#docs:sql:expressions:subqueries::all}
The `ALL` quantifier specifies that the comparison as a whole evaluates to `true` when the individual comparison results of _the expression at the left hand side of the comparison operator_ with each of the values from _the subquery at the right hand side of the comparison operator_ **all** evaluate to `true`:
```sql
SELECT 6 <= ALL (SELECT grade FROM grades) AS adequate;
```
returns:
| adequate |
|----------|
| true |
because 6 is less than or equal to each of the subquery results 7, 8 and 9.
However, the following query
```sql
SELECT 8 >= ALL (SELECT grade FROM grades) AS excellent;
```
returns
| excellent |
|-----------|
| false |
because 8 is not greater than or equal to the subquery result 7. And thus, because not all comparisons evaluate `true`, `>= ALL` as a whole evaluates to `false`.
#### `ANY` {#docs:sql:expressions:subqueries::any}
The `ANY` quantifier specifies that the comparison as a whole evaluates to `true` when at least one of the individual comparison results evaluates to `true`.
For example:
```sql
SELECT 5 >= ANY (SELECT grade FROM grades) AS fail;
```
returns
| fail |
|-------|
| false |
because no result of the subquery is less than or equal to 5.
The quantifier `SOME` maybe used instead of `ANY`: `ANY` and `SOME` are interchangeable. | 465 | [
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|
SQL | Expressions | `EXISTS` | <div id="rrdiagram2"></div>
The `EXISTS` operator tests for the existence of any row inside the subquery. It returns either true when the subquery returns one or more records, and false otherwise. The `EXISTS` operator is generally the most useful as a *correlated* subquery to express semijoin operations. However, it can be used as an uncorrelated subquery as well.
For example, we can use it to figure out if there are any grades present for a given course:
```sql
SELECT EXISTS (FROM grades WHERE course = 'Math') AS math_grades_present;
```
| math_grades_present |
|--------------------:|
| true |
```sql
SELECT EXISTS (FROM grades WHERE course = 'History') AS history_grades_present;
```
| history_grades_present |
|-----------------------:|
| false |
> The subqueries in the examples above make use of the fact that you can omit the `SELECT *` in DuckDB thanks to the [`FROM`-first syntax](#docs:sql:query_syntax:from). The `SELECT` clause is required in subqueries by other SQL systems but cannot fulfil any purpose in `EXISTS` and `NOT EXISTS` subqueries.
#### `NOT EXISTS` {#docs:sql:expressions:subqueries::not-exists}
The `NOT EXISTS` operator tests for the absence of any row inside the subquery. It returns either true when the subquery returns an empty result, and false otherwise. The `NOT EXISTS` operator is generally the most useful as a *correlated* subquery to express antijoin operations. For example, to find Person nodes without an interest:
```sql
CREATE TABLE Person (id BIGINT, name VARCHAR);
CREATE TABLE interest (PersonId BIGINT, topic VARCHAR);
INSERT INTO Person VALUES (1, 'Jane'), (2, 'Joe');
INSERT INTO interest VALUES (2, 'Music');
SELECT *
FROM Person
WHERE NOT EXISTS (FROM interest WHERE interest.PersonId = Person.id);
```
| id | name |
|---:|------|
| 1 | Jane |
> DuckDB automatically detects when a `NOT EXISTS` query expresses an antijoin operation. There is no need to manually rewrite such queries to use `LEFT OUTER JOIN ... WHERE ... IS NULL`. | 492 | [
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|
SQL | Expressions | `IN` Operator | <div id="rrdiagram3"></div>
The `IN` operator checks containment of the left expression inside the result defined by the subquery or the set of expressions on the right hand side (RHS). The `IN` operator returns true if the expression is present in the RHS, false if the expression is not in the RHS and the RHS has no `NULL` values, or `NULL` if the expression is not in the RHS and the RHS has `NULL` values.
We can use the `IN` operator in a similar manner as we used the `EXISTS` operator:
```sql
SELECT 'Math' IN (SELECT course FROM grades) AS math_grades_present;
```
| math_grades_present |
|--------------------:|
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|
SQL | Expressions | Correlated Subqueries | All the subqueries presented here so far have been **uncorrelated** subqueries, where the subqueries themselves are entirely self-contained and can be run without the parent query. There exists a second type of subqueries called **correlated** subqueries. For correlated subqueries, the subquery uses values from the parent subquery.
Conceptually, the subqueries are run once for every single row in the parent query. Perhaps a simple way of envisioning this is that the correlated subquery is a **function** that is applied to every row in the source data set.
For example, suppose that we want to find the minimum grade for every course. We could do that as follows:
```sql
SELECT *
FROM grades grades_parent
WHERE grade =
(SELECT min(grade)
FROM grades
WHERE grades.course = grades_parent.course);
```
| grade | course |
|------:|--------|
| 7 | Math |
| 8 | CS |
The subquery uses a column from the parent query (` grades_parent.course`). Conceptually, we can see the subquery as a function where the correlated column is a parameter to that function:
```sql
SELECT min(grade)
FROM grades
WHERE course = ?;
```
Now when we execute this function for each of the rows, we can see that for `Math` this will return `7`, and for `CS` it will return `8`. We then compare it against the grade for that actual row. As a result, the row `(Math, 9)` will be filtered out, as `9 <> 7`. | 332 | [
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|
SQL | Expressions | Returning Each Row of the Subquery as a Struct | Using the name of a subquery in the `SELECT` clause (without referring to a specific column) turns each row of the subquery into a struct whose fields correspond to the columns of the subquery. For example:
```sql
SELECT t
FROM (SELECT unnest(generate_series(41, 43)) AS x, 'hello' AS y) t;
```
<div class="narrow_table monospace_table"></div>
| t |
|-----------------------|
| {'x': 41, 'y': hello} |
| {'x': 42, 'y': hello} |
| {'x': 43, 'y': hello} | | 140 | [
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|
SQL | Functions | Function Syntax | <div id="rrdiagram"></div> | 9 | [
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|
SQL | Functions | Function Chaining via the Dot Operator | DuckDB supports the dot syntax for function chaining. This allows the function call `fn(arg1, arg2, arg3, ...)` to be rewritten as `arg1.fn(arg2, arg3, ...)`. For example, take the following use of the [`replace` function](#docs:sql:functions:char::replacestring-source-target):
```sql
SELECT replace(goose_name, 'goose', 'duck') AS duck_name
FROM unnest(['African goose', 'Faroese goose', 'Hungarian goose', 'Pomeranian goose']) breed(goose_name);
```
This can be rewritten as follows:
```sql
SELECT goose_name.replace('goose', 'duck') AS duck_name
FROM unnest(['African goose', 'Faroese goose', 'Hungarian goose', 'Pomeranian goose']) breed(goose_name);
``` | 183 | [
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] |
|
SQL | Functions | Query Functions | The `duckdb_functions()` table function shows the list of functions currently built into the system.
```sql
SELECT DISTINCT ON(function_name)
function_name,
function_type,
return_type,
parameters,
parameter_types,
description
FROM duckdb_functions()
WHERE function_type = 'scalar'
AND function_name LIKE 'b%'
ORDER BY function_name;
```
| function_name | function_type | return_type | parameters | parameter_types | description |
|---------------|---------------|-------------|------------------------|----------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| bar | scalar | VARCHAR | [x, min, max, width] | [DOUBLE, DOUBLE, DOUBLE, DOUBLE] | Draws a band whose width is proportional to (x - min) and equal to width characters when x = max. width defaults to 80 |
| base64 | scalar | VARCHAR | [blob] | [BLOB] | Convert a blob to a base64 encoded string |
| bin | scalar | VARCHAR | [value] | [VARCHAR] | Converts the value to binary representation |
| bit_count | scalar | TINYINT | [x] | [TINYINT] | Returns the number of bits that are set |
| bit_length | scalar | BIGINT | [col0] | [VARCHAR] | NULL |
| bit_position | scalar | INTEGER | [substring, bitstring] | [BIT, BIT] | Returns first starting index of the specified substring within bits, or zero if it is not present. The first (leftmost) bit is indexed 1 |
| bitstring | scalar | BIT | [bitstring, length] | [VARCHAR, INTEGER] | Pads the bitstring until the specified length |
> Currently, the description and parameter names of functions are not available in the `duckdb_functions()` function. | 413 | [
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|
SQL | Functions | Aggregate Functions | <!-- markdownlint-disable MD001 --> | 7 | [
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|
SQL | Functions | Examples | Produce a single row containing the sum of the `amount` column:
```sql
SELECT sum(amount)
FROM sales;
```
Produce one row per unique region, containing the sum of `amount` for each group:
```sql
SELECT region, sum(amount)
FROM sales
GROUP BY region;
```
Return only the regions that have a sum of `amount` higher than 100:
```sql
SELECT region
FROM sales
GROUP BY region
HAVING sum(amount) > 100;
```
Return the number of unique values in the `region` column:
```sql
SELECT count(DISTINCT region)
FROM sales;
```
Return two values, the total sum of `amount` and the sum of `amount` minus columns where the region is `north` using the [`FILTER` clause](#docs:sql:query_syntax:filter):
```sql
SELECT sum(amount), sum(amount) FILTER (region != 'north')
FROM sales;
```
Returns a list of all regions in order of the `amount` column:
```sql
SELECT list(region ORDER BY amount DESC)
FROM sales;
```
Returns the amount of the first sale using the `first()` aggregate function:
```sql
SELECT first(amount ORDER BY date ASC)
FROM sales;
``` | 267 | [
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|
SQL | Functions | Syntax | <div id="rrdiagram"></div>
Aggregates are functions that *combine* multiple rows into a single value. Aggregates are different from scalar functions and window functions because they change the cardinality of the result. As such, aggregates can only be used in the `SELECT` and `HAVING` clauses of a SQL query.
#### `DISTINCT` Clause in Aggregate Functions {#docs:sql:functions:aggregates::distinct-clause-in-aggregate-functions}
When the `DISTINCT` clause is provided, only distinct values are considered in the computation of the aggregate. This is typically used in combination with the `count` aggregate to get the number of distinct elements; but it can be used together with any aggregate function in the system.
#### `ORDER BY` Clause in Aggregate Functions {#docs:sql:functions:aggregates::order-by-clause-in-aggregate-functions}
An `ORDER BY` clause can be provided after the last argument of the function call. Note the lack of the comma separator before the clause.
```sql
SELECT ⟨aggregate_function⟩(⟨arg⟩, ⟨sep⟩ ORDER BY ⟨ordering_criteria⟩);
```
This clause ensures that the values being aggregated are sorted before applying the function.
Most aggregate functions are order-insensitive, and for them this clause is parsed and discarded.
However, there are some order-sensitive aggregates that can have non-deterministic results without ordering, e.g., `first`, `last`, `list` and `string_agg` / `group_concat` / `listagg`.
These can be made deterministic by ordering the arguments.
For example:
```sql
CREATE TABLE tbl AS
SELECT s FROM range(1, 4) r(s);
SELECT string_agg(s, ', ' ORDER BY s DESC) AS countdown
FROM tbl;
```
| countdown |
|-----------|
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|
SQL | Functions | General Aggregate Functions | The table below shows the available general aggregate functions.
| Function | Description |
|:--|:--------|
| [`any_value(arg)`](#::any_valuearg) | Returns the first non-null value from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arbitrary(arg)`](#::arbitraryarg) | Returns the first value (null or non-null) from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_max(arg, val)`](#::arg_maxarg-val) | Finds the row with the maximum `val` and calculates the `arg` expression at that row. Rows where the value of the `arg` or `val` expression is `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_max(arg, val, n)`](#::arg_maxarg-val-n) | The generalized case of [`arg_max`](#::arg_maxarg-val) for `n` values: returns a `LIST` containing the `arg` expressions for the top `n` rows ordered by `val` descending. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_max_null(arg, val)`](#::arg_max_nullarg-val) | Finds the row with the maximum `val` and calculates the `arg` expression at that row. Rows where the `val` expression evaluates to `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_min(arg, val)`](#::arg_minarg-val) | Finds the row with the minimum `val` and calculates the `arg` expression at that row. Rows where the value of the `arg` or `val` expression is `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_min(arg, val, n)`](#::arg_minarg-val-n) | Returns a `LIST` containing the `arg` expressions for the "bottom" `n` rows ordered by `val` ascending. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`arg_min_null(arg, val)`](#::arg_min_nullarg-val) | Finds the row with the minimum `val` and calculates the `arg` expression at that row. Rows where the `val` expression evaluates to `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`array_agg(arg)`](#::array_aggarg) | Returns a `LIST` containing all the values of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`avg(arg)`](#::avgarg) | Calculates the average of all non-null values in `arg`. |
| [`bit_and(arg)`](#::bit_andarg) | Returns the bitwise AND of all bits in a given expression. |
| [`bit_or(arg)`](#::bit_orarg) | Returns the bitwise OR of all bits in a given expression. |
| [`bit_xor(arg)`](#::bit_xorarg) | Returns the bitwise XOR of all bits in a given expression. |
| [`bitstring_agg(arg)`](#::bitstring_aggarg) | Returns a bitstring whose length corresponds to the range of the non-null (integer) values, with bits set at the location of each (distinct) value. |
| [`bool_and(arg)`](#::bool_andarg) | Returns `true` if every input value is `true`, otherwise `false`. |
| [`bool_or(arg)`](#::bool_orarg) | Returns `true` if any input value is `true`, otherwise `false`. |
| [`count()`](#::countarg) | Returns the number of rows in a group. |
| [`count(arg)`](#::countarg) | Returns the number of non-null values in `arg`. |
| [`favg(arg)`](#::favgarg) | Calculates the average using a more accurate floating point summation (Kahan Sum). |
| [`first(arg)`](#::firstarg) | Returns the first value (null or non-null) from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`fsum(arg)`](#::fsumarg) | Calculates the sum using a more accurate floating point summation (Kahan Sum). |
| [`geomean(arg)`](#::geomeanarg) | Calculates the geometric mean of all non-null values in `arg`. |
| [`histogram(arg)`](#::histogramarg) | Returns a `MAP` of key-value pairs representing buckets and counts. |
| [`last(arg)`](#::lastarg) | Returns the last value of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`list(arg)`](#::listarg) | Returns a `LIST` containing all the values of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`max(arg)`](#::maxarg) | Returns the maximum value present in `arg`. |
| [`max(arg, n)`](#::maxarg-n) | Returns a `LIST` containing the `arg` values for the "top" `n` rows ordered by `arg` descending. |
| [`max_by(arg, val)`](#::max_byarg-val) | Finds the row with the maximum `val`. Calculates the `arg` expression at that row. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`max_by(arg, val, n)`](#::max_byarg-val-n) | Returns a `LIST` containing the `arg` expressions for the "top" `n` rows ordered by `val` descending. |
| [`min(arg)`](#::minarg) | Returns the minimum value present in `arg`. |
| [`min(arg, n)`](#::minarg-n) | Returns a `LIST` containing the `arg` values for the "bottom" `n` rows ordered by `arg` ascending. |
| [`min_by(arg, val)`](#::min_byarg-val) | Finds the row with the minimum `val`. Calculates the `arg` expression at that row. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`min_by(arg, val, n)`](#::min_byarg-val-n) | Returns a `LIST` containing the `arg` expressions for the "bottom" `n` rows ordered by `val` ascending. |
| [`product(arg)`](#::productarg) | Calculates the product of all non-null values in `arg`. |
| [`string_agg(arg, sep)`](#::string_aggarg-sep) | Concatenates the column string values with a separator. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| [`sum(arg)`](#::sumarg) | Calculates the sum of all non-null values in `arg`. | | 1,570 | [
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|
SQL | Functions | General Aggregate Functions | `any_value(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the first non-`NULL` value from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `any_value(A)` |
| **Alias(es)** | - | | 79 | [
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SQL | Functions | General Aggregate Functions | `arbitrary(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the first value (` NULL` or non-`NULL`) from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arbitrary(A)` |
| **Alias(es)** | `first(A)` | | 86 | [
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SQL | Functions | General Aggregate Functions | `arg_max(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the maximum `val` and calculates the `arg` expression at that row. Rows where the value of the `arg` or `val` expression is `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_max(A, B)` |
| **Alias(es)** | `argMax(arg, val)`, `max_by(arg, val)` | | 124 | [
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SQL | Functions | General Aggregate Functions | `arg_max(arg, val, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The generalized case of [`arg_max`](#::arg_maxarg-val) for `n` values: returns a `LIST` containing the `arg` expressions for the top `n` rows ordered by `val` descending. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_max(A, B, 2)` |
| **Alias(es)** | `argMax(arg, val, n)`, `max_by(arg, val, n)` | | 135 | [
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SQL | Functions | General Aggregate Functions | `arg_max_null(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the maximum `val` and calculates the `arg` expression at that row. Rows where the `val` expression evaluates to `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_max_null(A, B)` |
| **Alias(es)** | - | | 105 | [
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SQL | Functions | General Aggregate Functions | `arg_min(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the minimum `val` and calculates the `arg` expression at that row. Rows where the value of the `arg` or `val` expression is `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_min(A, B)` |
| **Alias(es)** | `argmin(arg, val)`, `min_by(arg, val)` | | 124 | [
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SQL | Functions | General Aggregate Functions | `arg_min(arg, val, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The generalized case of [`arg_min`](#::arg_minarg-val) for `n` values: returns a `LIST` containing the `arg` expressions for the top `n` rows ordered by `val` descending. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_min(A, B, 2)` |
| **Alias(es)** | `argmin(arg, val, n)`, `min_by(arg, val, n)` | | 135 | [
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SQL | Functions | General Aggregate Functions | `arg_min_null(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the minimum `val` and calculates the `arg` expression at that row. Rows where the `val` expression evaluates to `NULL` are ignored. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `arg_min_null(A, B)` |
| **Alias(es)** | - | | 105 | [
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SQL | Functions | General Aggregate Functions | `array_agg(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `LIST` containing all the values of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `array_agg(A)` |
| **Alias(es)** | `list` | | 81 | [
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SQL | Functions | General Aggregate Functions | `avg(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the average of all non-null values in `arg`. |
| **Example** | `avg(A)` |
| **Alias(es)** | `mean` | | 61 | [
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SQL | Functions | General Aggregate Functions | `bit_and(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the bitwise `AND` of all bits in a given expression. |
| **Example** | `bit_and(A)` |
| **Alias(es)** | - | | 61 | [
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SQL | Functions | General Aggregate Functions | `bit_or(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the bitwise `OR` of all bits in a given expression. |
| **Example** | `bit_or(A)` |
| **Alias(es)** | - | | 61 | [
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SQL | Functions | General Aggregate Functions | `bit_xor(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the bitwise `XOR` of all bits in a given expression. |
| **Example** | `bit_xor(A)` |
| **Alias(es)** | - | | 62 | [
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SQL | Functions | General Aggregate Functions | `bitstring_agg(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a bitstring whose length corresponds to the range of the non-null (integer) values, with bits set at the location of each (distinct) value. |
| **Example** | `bitstring_agg(A)` |
| **Alias(es)** | - | | 80 | [
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SQL | Functions | General Aggregate Functions | `bool_and(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `true` if every input value is `true`, otherwise `false`. |
| **Example** | `bool_and(A)` |
| **Alias(es)** | - | | 63 | [
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SQL | Functions | General Aggregate Functions | `bool_or(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `true` if any input value is `true`, otherwise `false`. |
| **Example** | `bool_or(A)` |
| **Alias(es)** | - | | 63 | [
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SQL | Functions | General Aggregate Functions | `count()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the number of rows in a group.|
| **Example** | `count()` |
| **Alias(es)** | `count(*)` | | 57 | [
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SQL | Functions | General Aggregate Functions | `count(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the number of non-null values in `arg`. |
| **Example** | `count(A)` |
| **Alias(es)** | - | | 57 | [
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SQL | Functions | General Aggregate Functions | `favg(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the average using a more accurate floating point summation (Kahan Sum). |
| **Example** | `favg(A)` |
| **Alias(es)** | - | | 64 | [
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SQL | Functions | General Aggregate Functions | `first(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the first value (null or non-null) from `arg`. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `first(A)` |
| **Alias(es)** | `arbitrary(A)` | | 83 | [
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SQL | Functions | General Aggregate Functions | `fsum(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the sum using a more accurate floating point summation (Kahan Sum). |
| **Example** | `fsum(A)` |
| **Alias(es)** | `sumKahan`, `kahan_sum` | | 73 | [
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SQL | Functions | General Aggregate Functions | `geomean(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the geometric mean of all non-null values in `arg`. |
| **Example** | `geomean(A)` |
| **Alias(es)** | `geometric_mean(A)` | | 67 | [
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SQL | Functions | General Aggregate Functions | `histogram(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `MAP` of key-value pairs representing buckets and counts. |
| **Example** | `histogram(A)` |
| **Alias(es)** | - | | 61 | [
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SQL | Functions | General Aggregate Functions | `last(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the last value of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `last(A)` |
| **Alias(es)** | - | | 73 | [
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SQL | Functions | General Aggregate Functions | `list(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `LIST` containing all the values of a column. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `list(A)` |
| **Alias(es)** | `array_agg` | | 81 | [
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SQL | Functions | General Aggregate Functions | `max(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the maximum value present in `arg`. |
| **Example** | `max(A)` |
| **Alias(es)** | - | | 55 | [
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SQL | Functions | General Aggregate Functions | `max(arg, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** | Returns a `LIST` containing the `arg` values for the "top" `n` rows ordered by `arg` descending. |
| **Example** | `max(A, 2)` |
| **Alias(es)** | - | | 76 | [
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SQL | Functions | General Aggregate Functions | `max_by(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the maximum `val`. Calculates the `arg` expression at that row. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `max_by(A, B)` |
| **Alias(es)** | `argMax(arg, val)`, `arg_max(arg, val)` | | 102 | [
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SQL | Functions | General Aggregate Functions | `max_by(arg, val, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `LIST` containing the `arg` expressions for the "top" `n` rows ordered by `val` descending. |
| **Example** | `max_by_n(A, B, 2)` |
| **Alias(es)** | `argMax(arg, val, n)`, `arg_max(arg, val, n)` | | 98 | [
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SQL | Functions | General Aggregate Functions | `min(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the minimum value present in `arg`. |
| **Example** | `min(A)` |
| **Alias(es)** | - | | 55 | [
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SQL | Functions | General Aggregate Functions | `min(arg, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `LIST` containing the `arg` values for the "bottom" `n` rows ordered by `arg` ascending. |
| **Example** | `min(A, 2)` |
| **Alias(es)** | - | | 76 | [
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SQL | Functions | General Aggregate Functions | `min_by(arg, val)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Finds the row with the minimum `val`. Calculates the `arg` expression at that row. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `min_by(A, B)` |
| **Alias(es)** | `argMin(arg, val)`, `arg_min(arg, val)` | | 102 | [
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SQL | Functions | General Aggregate Functions | `min_by(arg, val, n)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `LIST` containing the `arg` expressions for the "bottom" `n` rows ordered by `val` ascending. |
| **Example** | `min_by(A, B, 2)` |
| **Alias(es)** | `argMin(arg, val, n)`, `arg_min(arg, val, n)` | | 97 | [
0.11890102177858353,
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SQL | Functions | General Aggregate Functions | `product(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the product of all non-null values in `arg`. |
| **Example** | `product(A)` |
| **Alias(es)** | - | | 59 | [
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SQL | Functions | General Aggregate Functions | `string_agg(arg, sep)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Concatenates the column string values with a separator. This function is [affected by ordering](#::order-by-clause-in-aggregate-functions). |
| **Example** | `string_agg(S, ',')` |
| **Alias(es)** | `group_concat(arg, sep)`, `listagg(arg, sep)` | | 94 | [
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SQL | Functions | General Aggregate Functions | `sum(arg)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Calculates the sum of all non-null values in `arg`. |
| **Example** | `sum(A)` |
| **Alias(es)** | - |
#### Handling `NULL` Values {#docs:sql:functions:aggregates::handling-null-values}
All general aggregate functions except for [`list`](#::listarg) and [`first`](#::firstarg) (and their aliases [`array_agg`](#::array_aggarg) and [`arbitrary`](#::arbitraryarg), respectively) ignore `NULL`s.
To exclude `NULL`s from `list`, you can use a [`FILTER` clause](#docs:sql:query_syntax:filter).
To ignore `NULL`s from `first`, you can use the [`any_value` aggregate](#::any_valuearg).
All general aggregate functions except [`count`](#::countarg) return `NULL` on empty groups and groups without non-`NULL` inputs.
In particular, [`list`](#::listarg) does *not* return an empty list, [`sum`](#::sumarg) does *not* return zero, and [`string_agg`](#::string_aggarg-sep) does *not* return an empty string in this case. | 282 | [
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] |
SQL | Functions | Approximate Aggregates | The table below shows the available approximate aggregate functions.
| Function | Description | Example |
|:---|:---|:---|
| `approx_count_distinct(x)` | Gives the approximate count of distinct elements using HyperLogLog. | `approx_count_distinct(A)` |
| `approx_quantile(x, pos)` | Gives the approximate quantile using T-Digest. | `approx_quantile(A, 0.5)` |
| `reservoir_quantile(x, quantile, sample_size = 8192)` | Gives the approximate quantile using reservoir sampling, the sample size is optional and uses 8192 as a default size. | `reservoir_quantile(A, 0.5, 1024)` | | 152 | [
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|
SQL | Functions | Statistical Aggregates | The table below shows the available statistical aggregate functions.
They all ignore `NULL` values (in the case of a single input column `x`), or pairs where either input is `NULL` (in the case of two input columns `y` and `x`).
| Function | Description |
|:--|:--------|
| [`corr(y, x)`](#::corry-x) | The correlation coefficient. |
| [`covar_pop(y, x)`](#::covar_popy-x) | The population covariance, which does not include bias correction. |
| [`covar_samp(y, x)`](#::covar_sampy-x) | The sample covariance, which includes Bessel's bias correction. |
| [`entropy(x)`](#::entropyx) | The log-2 entropy. |
| [`kurtosis_pop(x)`](#::kurtosis_popx) | The excess kurtosis (Fisher’s definition) without bias correction. |
| [`kurtosis(x)`](#::kurtosisx) | The excess kurtosis (Fisher's definition) with bias correction according to the sample size. |
| [`mad(x)`](#::madx) | The median absolute deviation. Temporal types return a positive `INTERVAL`. |
| [`median(x)`](#::medianx) | The middle value of the set. For even value counts, quantitative values are averaged and ordinal values return the lower value. |
| [`mode(x)`](#::modex)| The most frequent value. |
| [`quantile_cont(x, pos)`](#::quantile_contx-pos) | The interpolated `pos`-quantile of `x` for `0 <= pos <= 1`, i.e., orders the values of `x` and returns the `pos * (n_nonnull_values - 1)`th (zero-indexed) element (or an interpolation between the adjacent elements if the index is not an integer). If `pos` is a `LIST` of `FLOAT`s, then the result is a `LIST` of the corresponding interpolated quantiles. |
| [`quantile_disc(x, pos)`](#::quantile_discx-pos) | The discrete `pos`-quantile of `x` for `0 <= pos <= 1`, i.e., orders the values of `x` and returns the `greatest(ceil(pos * n_nonnull_values) - 1, 0)`th (zero-indexed) element. If `pos` is a `LIST` of `FLOAT`s, then the result is a `LIST` of the corresponding discrete quantiles. |
| [`regr_avgx(y, x)`](#::regr_avgxy-x) | The average of the independent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| [`regr_avgy(y, x)`](#::regr_avgyy-x) | The average of the dependent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| [`regr_count(y, x)`](#::regr_county-x) | The number of non-`NULL` pairs. |
| [`regr_intercept(y, x)`](#::regr_intercepty-x) | The intercept of the univariate linear regression line, where x is the independent variable and y is the dependent variable. |
| [`regr_r2(y, x)`](#::regr_r2y-x) | The squared Pearson correlation coefficient between y and x. Also: The coefficient of determination in a linear regression, where x is the independent variable and y is the dependent variable. |
| [`regr_slope(y, x)`](#::regr_slopey-x) | The slope of the linear regression line, where x is the independent variable and y is the dependent variable. |
| [`regr_sxx(y, x)`](#::regr_sxxy-x) | The population variance, which includes Bessel's bias correction, of the independent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| [`regr_sxy(y, x)`](#::regr_sxyy-x) | The population covariance, which includes Bessel's bias correction. |
| [`regr_syy(y, x)`](#::regr_syyy-x) | The population variance, which includes Bessel's bias correction, of the dependent variable for non-`NULL` pairs , where x is the independent variable and y is the dependent variable. |
| [`skewness(x)`](#::skewnessx) | The skewness. |
| [`stddev_pop(x)`](#::stddev_popx) | The population standard deviation. |
| [`stddev_samp(x)`](#::stddev_sampx) | The sample standard deviation. |
| [`var_pop(x)`](#::var_popx) | The population variance, which does not include bias correction. |
| [`var_samp(x)`](#::var_sampx) | The sample variance, which includes Bessel's bias correction. | | 1,094 | [
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|
SQL | Functions | Statistical Aggregates | `corr(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The correlation coefficient.
| **Formula** | `covar_pop(y, x) / (stddev_pop(x) * stddev_pop(y))` |
| **Alias(es)** | - | | 66 | [
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SQL | Functions | Statistical Aggregates | `covar_pop(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population covariance, which does not include bias correction. |
| **Formula** | `(sum(x*y) - sum(x) * sum(y) / regr_count(y, x)) / regr_count(y, x)`, `covar_samp(y, x) * (1 - 1 / regr_count(y, x))` |
| **Alias(es)** | - | | 107 | [
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SQL | Functions | Statistical Aggregates | `covar_samp(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The sample covariance, which includes Bessel's bias correction. |
| **Formula** | `(sum(x*y) - sum(x) * sum(y) / regr_count(y, x)) / (regr_count(y, x) - 1)`, `covar_pop(y, x) / (1 - 1 / regr_count(y, x))` |
| **Alias(es)** | `regr_sxy(y, x)` | | 120 | [
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SQL | Functions | Statistical Aggregates | `entropy(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The log-2 entropy. |
| **Formula** | - |
| **Alias(es)** | - | | 49 | [
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SQL | Functions | Statistical Aggregates | `kurtosis_pop(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The excess kurtosis (Fisher’s definition) without bias correction. |
| **Formula** | - |
| **Alias(es)** | - | | 58 | [
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SQL | Functions | Statistical Aggregates | `kurtosis(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The excess kurtosis (Fisher's definition) with bias correction according to the sample size. |
| **Formula** | - |
| **Alias(es)** | - | | 63 | [
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SQL | Functions | Statistical Aggregates | `mad(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The median absolute deviation. Temporal types return a positive `INTERVAL`. |
| **Formula** | `median(abs(x - median(x)))` |
| **Alias(es)** | - | | 66 | [
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SQL | Functions | Statistical Aggregates | `median(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The middle value of the set. For even value counts, quantitative values are averaged and ordinal values return the lower value. |
| **Formula** | `quantile_cont(x, 0.5)` |
| **Alias(es)** | - | | 77 | [
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SQL | Functions | Statistical Aggregates | `mode(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The most frequent value. |
| **Formula** | - |
| **Alias(es)** | - | | 48 | [
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SQL | Functions | Statistical Aggregates | `quantile_cont(x, pos)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The interpolated `pos`-quantile of `x` for `0 <= pos <= 1`, i.e., orders the values of `x` and returns the `pos * (n_nonnull_values - 1)`th (zero-indexed) element (or an interpolation between the adjacent elements if the index is not an integer). If `pos` is a `LIST` of `FLOAT`s, then the result is a `LIST` of the corresponding interpolated quantiles. |
| **Formula** | - |
| **Alias(es)** | - | | 141 | [
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SQL | Functions | Statistical Aggregates | `quantile_disc(x, pos)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The discrete `pos`-quantile of `x` for `0 <= pos <= 1`, i.e., orders the values of `x` and returns the `greatest(ceil(pos * n_nonnull_values) - 1, 0)`th (zero-indexed) element. If `pos` is a `LIST` of `FLOAT`s, then the result is a `LIST` of the corresponding discrete quantiles. |
| **Formula** | - |
| **Alias(es)** | `quantile` | | 136 | [
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SQL | Functions | Statistical Aggregates | `regr_avgx(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The average of the independent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| **Formula** | - |
| **Alias(es)** | - | | 70 | [
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SQL | Functions | Statistical Aggregates | `regr_avgy(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The average of the dependent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| **Formula** | - |
| **Alias(es)** | - | | 70 | [
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SQL | Functions | Statistical Aggregates | `regr_count(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The number of non-`NULL` pairs. |
| **Formula** | - |
| **Alias(es)** | - | | 53 | [
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SQL | Functions | Statistical Aggregates | `regr_intercept(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The intercept of the univariate linear regression line, where x is the independent variable and y is the dependent variable. |
| **Formula** | `regr_avgy(y, x) - regr_slope(y, x) * regr_avgx(y, x)` |
| **Alias(es)** | - | | 91 | [
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SQL | Functions | Statistical Aggregates | `regr_r2(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The squared Pearson correlation coefficient between y and x. Also: The coefficient of determination in a linear regression, where x is the independent variable and y is the dependent variable. |
| **Formula** | - |
| **Alias(es)** | - | | 77 | [
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SQL | Functions | Statistical Aggregates | `regr_slope(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the slope of the linear regression line, where x is the independent variable and y is the dependent variable. |
| **Formula** | `regr_sxy(y, x) / regr_sxx(y, x)` |
| **Alias(es)** | - | | 82 | [
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SQL | Functions | Statistical Aggregates | `regr_sxx(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population variance, which includes Bessel's bias correction, of the independent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| **Formula** | - |
| **Alias(es)** | - | | 80 | [
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SQL | Functions | Statistical Aggregates | `regr_sxy(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population covariance, which includes Bessel's bias correction. |
| **Formula** | - |
| **Alias(es)** | - | | 55 | [
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SQL | Functions | Statistical Aggregates | `regr_syy(y, x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population variance, which includes Bessel's bias correction, of the dependent variable for non-`NULL` pairs, where x is the independent variable and y is the dependent variable. |
| **Formula** | - |
| **Alias(es)** | - | | 80 | [
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SQL | Functions | Statistical Aggregates | `skewness(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The skewness. |
| **Formula** | - |
| **Alias(es)** | - | | 47 | [
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SQL | Functions | Statistical Aggregates | `stddev_pop(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population standard deviation. |
| **Formula** | `sqrt(var_pop(x))` |
| **Alias(es)** | - | | 54 | [
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SQL | Functions | Statistical Aggregates | `stddev_samp(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The sample standard deviation. |
| **Formula** | `sqrt(var_samp(x))`|
| **Alias(es)** | `stddev(x)`| | 59 | [
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SQL | Functions | Statistical Aggregates | `var_pop(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The population variance, which does not include bias correction. |
| **Formula** | `(sum(x^2) - sum(x)^2 / count(x)) / count(x)`, `var_samp(y, x) * (1 - 1 / count(x))` |
| **Alias(es)** | - | | 92 | [
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SQL | Functions | Statistical Aggregates | `var_samp(x)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The sample variance, which includes Bessel's bias correction. |
| **Formula** | `(sum(x^2) - sum(x)^2 / count(x)) / (count(x) - 1)`, `var_pop(y, x) / (1 - 1 / count(x))` |
| **Alias(es)** | `variance(arg, val)` | | 103 | [
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] |
SQL | Functions | Ordered Set Aggregate Functions | The table below shows the available “ordered set” aggregate functions.
These functions are specified using the `WITHIN GROUP (ORDER BY sort_expression)` syntax,
and they are converted to an equivalent aggregate function that takes the ordering expression
as the first argument.
| Function | Equivalent |
|:---|:---|
| <code>mode() WITHIN GROUP (ORDER BY column [(ASC|DESC)])</code> | <code>mode(column ORDER BY column [(ASC|DESC)])</code> |
| <code>percentile_cont(fraction) WITHIN GROUP (ORDER BY column [(ASC|DESC)])</code> | <code>quantile_cont(column, fraction ORDER BY column [(ASC|DESC)])</code> |
| <code>percentile_cont(fractions) WITHIN GROUP (ORDER BY column [(ASC|DESC)])</code> | <code>quantile_cont(column, fractions ORDER BY column [(ASC|DESC)])</code> |
| <code>percentile_disc(fraction) WITHIN GROUP (ORDER BY column [(ASC|DESC)])</code> | <code>quantile_disc(column, fraction ORDER BY column [(ASC|DESC)])</code> |
| <code>percentile_disc(fractions) WITHIN GROUP (ORDER BY column [(ASC|DESC)])</code> | <code>quantile_disc(column, fractions ORDER BY column [(ASC|DESC)])</code> | | 313 | [
0.11890102177858353,
0.01533113420009613,
-0.01635786145925522,
0.02390380948781967,
0.0552239827811718,
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0.0003144664515275508,
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|
SQL | Functions | Miscellaneous Aggregate Functions | | Function | Description | Alias |
|:--|:---|:--|
| `grouping()` | For queries with `GROUP BY` and either [`ROLLUP` or `GROUPING SETS`](#docs:sql:query_syntax:grouping_sets::identifying-grouping-sets-with-grouping_id): Returns an integer identifying which of the argument expressions where used to group on to create the current supper-aggregate row. | `grouping_id()` | | 96 | [
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|
SQL | Functions | Array Functions | <!-- markdownlint-disable MD001 -->
All [`LIST` functions](#docs:sql:functions:list) work with the [`ARRAY` data type](#docs:sql:data_types:array). Additionally, several `ARRAY`-native functions are also supported. | 53 | [
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|
SQL | Functions | Array-Native Functions | | Function | Description |
|----|-----|-------|---|
| [`array_value(index)`](#::array_valueindex) | Create an `ARRAY` containing the argument values. |
| [`array_cross_product(array1, array2)`](#::array_cross_productarray1-array2) | Compute the cross product of two arrays of size 3. The array elements can not be `NULL`. |
| [`array_cosine_similarity(array1, array2)`](#::array_cosine_similarityarray1-array2) | Compute the cosine similarity between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. |
[`array_cosine_distance(array1, array2)`](#::array_cosine_distancearray1-array2) | Compute the cosine distance between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. This is equivalent to `1.0 - array_cosine_similarity` |
| [`array_distance(array1, array2)`](#::array_distancearray1-array2) | Compute the distance between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. |
| [`array_inner_product(array1, array2)`](#::array_inner_productarray1-array2) | Compute the inner product between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. |
| [`array_negative_inner_product(array1, array2)`](#::array_negative_inner_productarray1-array2) | Compute the negative inner product between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. This is equivalent to `-array_inner_product` |
| [`array_dot_product(array1, array2)`](#::array_dot_productarray1-array2) | Alias for `array_inner_product(array1, array2)`. |
| [`array_negative_dot_product(array1, array2)`](#::array_negative_dot_productarray1-array2) | Alias for `array_negative_inner_product(array1, array2)`. | | 519 | [
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|
SQL | Functions | Array-Native Functions | `array_value(index)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Create an `ARRAY` containing the argument values. |
| **Example** | `array_value(1.0::FLOAT, 2.0::FLOAT, 3.0::FLOAT)` |
| **Result** | `[1.0, 2.0, 3.0]` | | 90 | [
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SQL | Functions | Array-Native Functions | `array_cross_product(array1, array2)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Compute the cross product of two arrays of size 3. The array elements can not be `NULL`. |
| **Example** | `array_cross_product(array_value(1.0::FLOAT, 2.0::FLOAT, 3.0::FLOAT), array_value(2.0::FLOAT, 3.0::FLOAT, 4.0::FLOAT))` |
| **Result** | `[-1.0, 2.0, -1.0]` | | 129 | [
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] |
SQL | Functions | Array-Native Functions | `array_cosine_similarity(array1, array2)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Compute the cosine similarity between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. |
| **Example** | `array_cosine_similarity(array_value(1.0::FLOAT, 2.0::FLOAT, 3.0::FLOAT), array_value(2.0::FLOAT, 3.0::FLOAT, 4.0::FLOAT))` |
| **Result** | `0.9925833` | | 138 | [
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SQL | Functions | Array-Native Functions | `array_cosine_distance(array1, array2)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Compute the cosine distance between two arrays of the same size. The array elements can not be `NULL`. The arrays can have any size as long as the size is the same for both arguments. This is equivalent to `1.0 - array_cosine_similarity`. |
| **Example** | `array_cosine_distance(array_value(1.0::FLOAT, 2.0::FLOAT, 3.0::FLOAT), array_value(2.0::FLOAT, 3.0::FLOAT, 4.0::FLOAT))` |
| **Result** | `0.007416606` | | 152 | [
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Subsets and Splits