# GROUP BY clause (Databricks SQL)

The `GROUP BY`

clause is used to group the rows based on a set of specified grouping expressions and compute aggregations on the group of rows based on one or more specified aggregate functions.
Databricks SQL also supports advanced aggregations to do multiple aggregations for the same input record set via `GROUPING SETS`

, `CUBE`

, `ROLLUP`

clauses.
The grouping expressions and advanced aggregations can be mixed in the `GROUP BY`

clause and nested in a `GROUPING SETS`

clause.

See more details in the Mixed/Nested Grouping Analytics section.

When a `FILTER`

clause is attached to an aggregate function, only the matching rows are passed to that function.

## Syntax

```
GROUP BY group_expression [, ...] [ WITH ROLLUP | WITH CUBE ]
GROUP BY { group_expression | { ROLLUP | CUBE | GROUPING SETS } ( grouping_set [, ...] ) } [, ...]
grouping_set
{ expression |
( [ expression [, ...] ] ) }
```

While aggregate functions are defined as

```
aggregate_name ( [ DISTINCT ] expression [, ...] ) [ FILTER ( WHERE boolean_expression ) ]
```

## Parameters

**group_expression**Specifies the criteria for grouping rows together. The grouping of rows is performed based on result values of the grouping expressions. A grouping expression may be a column name like

`GROUP BY a, a`

, column position like`GROUP BY 0`

, or an expression like`GROUP BY a + b`

.**grouping_set**A grouping set is specified by zero or more comma-separated expressions in parentheses. When the grouping set has only one element, parentheses can be omitted. For example, GROUPING SETS ((a), (b)) is the same as GROUPING SETS (a, b).

**GROUPING SETS**Groups the rows for each grouping set specified after

`GROUPING SETS`

. For example:`GROUP BY GROUPING SETS ((warehouse), (product))`

is semantically equivalent to a union of results of`GROUP BY warehouse`

and`GROUP BY product`

.This clause is a shorthand for a

`UNION ALL`

where each leg of the`UNION ALL`

operator performs aggregation of each grouping set specified in the`GROUPING SETS`

clause.Similarly,

`GROUP BY GROUPING SETS ((warehouse, product), (product), ())`

is semantically equivalent to the union of results of`GROUP BY warehouse, product`

,`GROUP BY product`

and a global aggregate.

Note

For Hive compatibility Databricks SQL allows `GROUP BY ... GROUPING SETS (...)`

. The `GROUP BY`

expressions are usually ignored, but if they contain extra expressions in addition to the `GROUPING SETS`

expressions, the extra expressions will be included in the grouping expressions and the value is always null. For example, `SELECT a, b, c FROM ... GROUP BY a, b, c GROUPING SETS (a, b)`

, the output of column c is always null.

**ROLLUP**Specifies multiple levels of aggregations in a single statement. This clause is used to compute aggregations based on multiple grouping sets.

`ROLLUP`

is a shorthand for`GROUPING SETS`

. For example:`GROUP BY warehouse, product WITH ROLLUP`

or`GROUP BY ROLLUP(warehouse, product)`

is equivalent to`GROUP BY GROUPING SETS((warehouse, product), (warehouse), ())`

.While

`GROUP BY ROLLUP(warehouse, product, (warehouse, location))`

is equivalent to

`GROUP BY GROUPING SETS((warehouse, product, location), (warehouse, product), (warehouse), ())`

.The N elements of a

`ROLLUP`

specification result in N+1`GROUPING SETS`

.**CUBE**The

`CUBE`

clause is used to perform aggregations based on a combination of grouping columns specified in the`GROUP BY`

clause.`CUBE`

is a shorthand for`GROUPING SETS`

. For example:`GROUP BY warehouse, product WITH CUBE`

or`GROUP BY CUBE(warehouse, product)`

is equivalent to`GROUP BY GROUPING SETS((warehouse, product), (warehouse), (product), ())`

.While

`GROUP BY CUBE(warehouse, product, (warehouse, location))`

is equivalent to

`GROUP BY GROUPING SETS((warehouse, product, location), (warehouse, product), (warehouse, location), (product, warehouse, location), (warehouse), (product), (warehouse, product), ())`

.The N elements of a

`CUBE`

specification results in 2^N`GROUPING SETS`

.**aggregate_name**An aggregate function name (MIN, MAX, COUNT, SUM, AVG, etc.).

**DISTINCT**Removes duplicates in input rows before they are passed to aggregate functions.

**FILTER**Filters the input rows for which the

`boolean_expression`

in the`WHERE`

clause evaluates to true are passed to the aggregate function; other rows are discarded.

## Mixed/Nested Grouping Analytics

A `GROUP BY`

clause can include multiple group_expressions and multiple `CUBE`

, `ROLLUP`

, and `GROUPING SETS`

s.

`GROUPING SETS`

can also have nested `CUBE`

, `ROLLUP`

, or `GROUPING SETS`

clauses. For example:

`GROUPING SETS(ROLLUP(warehouse, location), CUBE(warehouse, location)), GROUPING SETS(warehouse, GROUPING SETS(location, GROUPING SETS(ROLLUP(warehouse, location), CUBE(warehouse, location))))`

`CUBE`

and `ROLLUP`

is just syntax sugar for `GROUPING SETS`

.
Please refer to the sections above for how to translate `CUBE`

and `ROLLUP`

to `GROUPING SETS`

.
`group_expression`

can be treated as a single-group `GROUPING SETS`

in this context.

For multiple `GROUPING SETS`

in the `GROUP BY`

clause, Databricks SQL generates a single `GROUPING SETS`

by doing a cross-product of the original `GROUPING SETS`

.

For nested `GROUPING SETS`

in the `GROUPING SETS`

clause, Databricks SQL simply takes its grouping sets and strips them. For example:

`GROUP BY warehouse, GROUPING SETS((product), ()), GROUPING SETS((location, size), (location), (size), ())`

and

`GROUP BY warehouse, ROLLUP(product), CUBE(location, size)`

are equivalent to `GROUP BY GROUPING SETS( (warehouse, product, location, size), (warehouse, product, location), (warehouse, product, size), (warehouse, product), (warehouse, location, size), (warehouse, location), (warehouse, size), (warehouse))`

.

While `GROUP BY GROUPING SETS(GROUPING SETS(warehouse), GROUPING SETS((warehouse, product)))`

is equivalent to `GROUP BY GROUPING SETS((warehouse), (warehouse, product))`

.

## Examples

```
CREATE TEMP VIEW dealer (id, city, car_model, quantity) AS
VALUES (100, 'Fremont', 'Honda Civic', 10),
(100, 'Fremont', 'Honda Accord', 15),
(100, 'Fremont', 'Honda CRV', 7),
(200, 'Dublin', 'Honda Civic', 20),
(200, 'Dublin', 'Honda Accord', 10),
(200, 'Dublin', 'Honda CRV', 3),
(300, 'San Jose', 'Honda Civic', 5),
(300, 'San Jose', 'Honda Accord', 8);
-- Sum of quantity per dealership. Group by `id`.
> SELECT id, sum(quantity) FROM dealer GROUP BY id ORDER BY id;
id sum(quantity)
--- -------------
100 32
200 33
300 13
-- Use column position in GROUP by clause.
> SELECT id, sum(quantity) FROM dealer GROUP BY 1 ORDER BY 1;
id sum(quantity)
--- -------------
100 32
200 33
300 13
-- Multiple aggregations.
-- 1. Sum of quantity per dealership.
-- 2. Max quantity per dealership.
> SELECT id, sum(quantity) AS sum, max(quantity) AS max
FROM dealer GROUP BY id ORDER BY id;
id sum max
--- --- ---
100 32 15
200 33 20
300 13 8
-- Count the number of distinct dealers in cities per car_model.
> SELECT car_model, count(DISTINCT city) AS count FROM dealer GROUP BY car_model;
car_model count
------------ -----
Honda Civic 3
Honda CRV 2
Honda Accord 3
-- Sum of only 'Honda Civic' and 'Honda CRV' quantities per dealership.
> SELECT id,
sum(quantity) FILTER (WHERE car_model IN ('Honda Civic', 'Honda CRV')) AS `sum(quantity)`
FROM dealer
GROUP BY id ORDER BY id;
id sum(quantity)
--- -------------
100 17
200 23
300 5
-- Aggregations using multiple sets of grouping columns in a single statement.
-- Following performs aggregations based on four sets of grouping columns.
-- 1. city, car_model
-- 2. city
-- 3. car_model
-- 4. Empty grouping set. Returns quantities for all city and car models.
> SELECT city, car_model, sum(quantity) AS sum
FROM dealer
GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ())
ORDER BY city;
city car_model sum
--------- ------------ ---
null null 78
null HondaAccord 33
null HondaCRV 10
null HondaCivic 35
Dublin null 33
Dublin HondaAccord 10
Dublin HondaCRV 3
Dublin HondaCivic 20
Fremont null 32
Fremont HondaAccord 15
Fremont HondaCRV 7
Fremont HondaCivic 10
San Jose null 13
San Jose HondaAccord 8
San Jose HondaCivic 5
-- Group by processing with `ROLLUP` clause.
-- Equivalent GROUP BY GROUPING SETS ((city, car_model), (city), ())
> SELECT city, car_model, sum(quantity) AS sum
FROM dealer
GROUP BY city, car_model WITH ROLLUP
ORDER BY city, car_model;
city car_model sum
--------- ------------ ---
null null 78
Dublin null 33
Dublin HondaAccord 10
Dublin HondaCRV 3
Dublin HondaCivic 20
Fremont null 32
Fremont HondaAccord 15
Fremont HondaCRV 7
Fremont HondaCivic 10
San Jose null 13
San Jose HondaAccord 8
San Jose HondaCivic 5
-- Group by processing with `CUBE` clause.
-- Equivalent GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ())
> SELECT city, car_model, sum(quantity) AS sum
FROM dealer
GROUP BY city, car_model WITH CUBE
ORDER BY city, car_model;
city car_model sum
--------- ------------ ---
null null 78
null HondaAccord 33
null HondaCRV 10
null HondaCivic 35
Dublin null 33
Dublin HondaAccord 10
Dublin HondaCRV 3
Dublin HondaCivic 20
Fremont null 32
Fremont HondaAccord 15
Fremont HondaCRV 7
Fremont HondaCivic 10
San Jose null 13
San Jose HondaAccord 8
San Jose HondaCivic 5
--Prepare data for ignore nulls example
> CREATE TEMP VIEW person (id, name, age) AS
VALUES (100, 'Mary', NULL),
(200, 'John', 30),
(300, 'Mike', 80),
(400, 'Dan' , 50);
--Select the first row in column age
> SELECT FIRST(age) FROM person;
first(age, false)
--------------------
NULL
--Get the first row in column `age` ignore nulls,last row in column `id` and sum of column `id`.
> SELECT FIRST(age IGNORE NULLS), LAST(id), SUM(id) FROM person;
first(age, true) last(id, false) sum(id)
------------------- ------------------ ----------
30 400 1000
```