28 lines
1.1 KiB
MySQL
28 lines
1.1 KiB
MySQL
|
|
||
|
|
-- CLUSTER BY { expression [ , ... ] }
|
||
|
|
|
||
|
|
CREATE TABLE person (name STRING, age INT);
|
||
|
|
INSERT INTO person VALUES
|
||
|
|
('Zen Hui', 25),
|
||
|
|
('Anil B', 18),
|
||
|
|
('Shone S', 16),
|
||
|
|
('Mike A', 25),
|
||
|
|
('John A', 18),
|
||
|
|
('Jack N', 16);
|
||
|
|
|
||
|
|
-- Reduce the number of shuffle partitions to 2 to illustrate the behavior of `CLUSTER BY`.
|
||
|
|
-- It's easier to see the clustering and sorting behavior with less number of partitions.
|
||
|
|
SET spark.sql.shuffle.partitions = 2;
|
||
|
|
|
||
|
|
-- Select the rows with no ordering. Please note that without any sort directive, the results
|
||
|
|
-- of the query is not deterministic. It's included here to show the difference in behavior
|
||
|
|
-- of a query when `CLUSTER BY` is not used vs when it's used. The query below produces rows
|
||
|
|
-- where age column is not sorted.
|
||
|
|
SELECT name, age FROM person;
|
||
|
|
|
||
|
|
-- Produces rows clustered by age. Persons with same age are clustered together.
|
||
|
|
-- In the query below, persons with age 18 and 25 are in first partition and the
|
||
|
|
-- persons with age 16 are in the second partition. The rows are sorted based
|
||
|
|
-- on age within each partition.
|
||
|
|
SELECT age, name FROM person CLUSTER BY age;
|