GROUP BY Clause
Description
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. Spark 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 [ , group_expression [ , ... ] ] [ WITH { ROLLUP | CUBE } ]
GROUP BY { group_expression | { ROLLUP | CUBE | GROUPING SETS } (grouping_set [ , ...]) } [ , ... ]
While aggregate functions are defined as
aggregate_name ( [ DISTINCT ] expression [ , ... ] ) [ FILTER ( WHERE boolean_expression ) ]
Parameters
-
group_expression
Specifies the criteria based on which the rows are grouped 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 likeGROUP BY 0
, or an expression likeGROUP 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 asGROUPING SETS (a, b)
.Syntax:
{ ( [ expression [ , ... ] ] ) | expression }
-
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 union of results ofGROUP BY warehouse
andGROUP BY product
. This clause is a shorthand for aUNION ALL
where each leg of theUNION ALL
operator performs aggregation of each grouping set specified in theGROUPING SETS
clause. Similarly,GROUP BY GROUPING SETS ((warehouse, product), (product), ())
is semantically equivalent to the union of results ofGROUP BY warehouse, product
,GROUP BY product
and global aggregate.Note: For Hive compatibility Spark allows
GROUP BY ... GROUPING SETS (...)
. The GROUP BY expressions are usually ignored, but if it contains extra expressions than 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 columnc
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 forGROUPING SETS
. For example,GROUP BY warehouse, product WITH ROLLUP
orGROUP BY ROLLUP(warehouse, product)
is equivalent toGROUP BY GROUPING SETS((warehouse, product), (warehouse), ())
.GROUP BY ROLLUP(warehouse, product, (warehouse, location))
is equivalent toGROUP BY GROUPING SETS((warehouse, product, location), (warehouse, product), (warehouse), ())
. The N elements of aROLLUP
specification results in N+1GROUPING SETS
. -
CUBE
CUBE
clause is used to perform aggregations based on combination of grouping columns specified in theGROUP BY
clause.CUBE
is a shorthand forGROUPING SETS
. For example,GROUP BY warehouse, product WITH CUBE
orGROUP BY CUBE(warehouse, product)
is equivalent toGROUP BY GROUPING SETS((warehouse, product), (warehouse), (product), ())
.GROUP BY CUBE(warehouse, product, (warehouse, location))
is equivalent toGROUP BY GROUPING SETS((warehouse, product, location), (warehouse, product), (warehouse, location), (product, warehouse, location), (warehouse), (product), (warehouse, product), ())
. The N elements of aCUBE
specification results in 2^NGROUPING SETS
. -
Mixed/Nested Grouping Analytics
A GROUP BY clause can include multiple
group_expression
s and multipleCUBE|ROLLUP|GROUPING SETS
s.GROUPING SETS
can also have nestedCUBE|ROLLUP|GROUPING SETS
clauses, e.g.GROUPING SETS(ROLLUP(warehouse, location), CUBE(warehouse, location))
,GROUPING SETS(warehouse, GROUPING SETS(location, GROUPING SETS(ROLLUP(warehouse, location), CUBE(warehouse, location))))
.CUBE|ROLLUP
is just a syntax sugar forGROUPING SETS
, please refer to the sections above for how to translateCUBE|ROLLUP
toGROUPING SETS
.group_expression
can be treated as a single-groupGROUPING SETS
under this context. For multipleGROUPING SETS
in theGROUP BY
clause, we generate a singleGROUPING SETS
by doing a cross-product of the originalGROUPING SETS
s. For nestedGROUPING SETS
in theGROUPING SETS
clause, we simply take its grouping sets and strip it. For example,GROUP BY warehouse, GROUPING SETS((product), ()), GROUPING SETS((location, size), (location), (size), ())
andGROUP BY warehouse, ROLLUP(product), CUBE(location, size)
is equivalent toGROUP BY GROUPING SETS( (warehouse, product, location, size), (warehouse, product, location), (warehouse, product, size), (warehouse, product), (warehouse, location, size), (warehouse, location), (warehouse, size), (warehouse))
.GROUP BY GROUPING SETS(GROUPING SETS(warehouse), GROUPING SETS((warehouse, product)))
is equivalent toGROUP BY GROUPING SETS((warehouse), (warehouse, product))
. -
aggregate_name
Specifies 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 theWHERE
clause evaluates to true are passed to the aggregate function; other rows are discarded.
Examples
CREATE TABLE dealer (id INT, city STRING, car_model STRING, quantity INT);
INSERT INTO dealer 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 dealer 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 TABLE person (id INT, name STRING, age INT);
INSERT INTO person 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 |
+-------------------+------------------+----------+