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Explore window functions in MariaDB Server. This section details SQL functions that perform calculations across a set of table rows related to the current row, enabling advanced analytical queries.
Use aggregate functions over windows. Standard aggregates like SUM and AVG can be used with an OVER clause to calculate running totals and moving averages.
It is possible to use aggregate functions as window functions. An aggregate function used as a window function must have the OVER clause. For example, here's COUNT() used as a window function:
MariaDB currently allows these aggregate functions to be used as window functions:
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SELECT COUNT(*) OVER (ORDER BY column) FROM table;Assign a sequential integer to rows. This function numbers rows within a partition starting at 1, based on the specified order.
ROW_NUMBER() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)ROW_NUMBER() is a that displays the number of a given row, starting at one and following the sequence of the window function, with identical values receiving different row numbers. It is similar to the and functions except that in that function, identical values will receive the same rank for each result.
The distinction between , and ROW_NUMBER():
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Return the value from the N-th row. This function retrieves the value of an expression from the N-th row within the window frame.
NTH_VALUE (expr[, num_row]) OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)The NTH_VALUE function returns the value evaluated at row number num_row of the window frame, starting from 1, or NULL if the row does not exist.
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Understand window function concepts. These functions operate on a set of rows (window) defined by an OVER clause, returning a value for each row without collapsing results.
Window functions perform calculations across a set of rows (in a defined window).
Dedicated window functions include
Aggregate functions that can also be used as window functions include
Window function queries are characterised by the OVER keyword, following which the set of rows used for the calculation is specified. By default, the set of rows used for the calculation (the "window) is the entire dataset, which can be ordered with the ORDER BY clause. The PARTITION BY clause is used to reduce the window to a particular group within the dataset.
Consider the following data:
The following two queries return the average partitioned by test and by name respectively:
It is also possible to specify which rows to include for the window function (for example, the current row and all preceding rows). See Window Frames for more details.
Window functions were introduced in SQL:2003, and their definition was expanded in subsequent versions of the standard. The last expansion was in the latest version of the standard, SQL:2011.
Most database products support a subset of the standard, they implement some functions defined as late as in SQL:2011, and at the same time leave some parts of SQL:2008 unimplemented.
MariaDB:
Supports ROWS and RANGE-type frames
All kinds of frame bounds are supported, including RANGE PRECEDING|FOLLOWING n frame bounds (unlike PostgreSQL or MS SQL Server)
Does not yet support DATE[TIME] datatype and arithmetic for RANGE-type frames (MDEV-9727)
Does not support GROUPS-type frames (it seems that no popular database supports it, either)
Does not support frame exclusion (no other database seems to support it, either) ()
Does not support explicit NULLS FIRST or NULLS LAST.
Does not support nested navigation in window functions (this is VALUE_OF(expr AT row_marker [, default_value) syntax)
The following window functions are supported:
"Streamable" window functions: , , ,
Window functions that can be streamed once the number of rows in partition is known: , ,
Aggregate functions that are currently supported as window functions are: , , , , , .
Aggregate functions with the DISTINCT specifier (e.g. COUNT( DISTINCT x)) are not supported as window functions.
MDEV-6115 is the main jira task for window functions development. Other tasks are attached as sub-tasks.
bb-10.2-mdev9543 is the feature tree for window functions. Development is ongoing, and this tree has the newest changes.
Test cases are in mysql-test/t/win*.test .
Given the following sample data:
First, let's order the records by email alphabetically, giving each an ascending rnum value starting with 1. This will make use of the ROW_NUMBER window function:
We can generate separate sequences based on account type, using the PARTITION BY clause:
Given the following structure and data, we want to find the top 5 salaries from each department.
We could do this without using window functions, as follows:
This has a number of disadvantages:
If there is no index, the query could take a long time if the employee_salary_table is large.
Adding and maintaining indexes adds overhead, and even with indexes on dept and salary, each subquery execution adds overhead by performing a lookup through the index.
Let's try achieve the same with window functions. First, generate a rank for all employees, using the RANK function.
Each department has a separate sequence of ranks due to the PARTITION BY clause. This particular sequence of values for rank() is given by the ORDER BY clause inside the window function’s OVER clause. Finally, to get our results in a readable format we order the data by dept and the newly generated ranking column.
Now, we need to reduce the results to find only the top 5 per department. Here is a common mistake:
Trying to filter only the first 5 values per department by putting a where clause in the statement does not work, due to the way window functions are computed. The computation of window functions happens after all WHERE, GROUP BY and HAVING clauses have been completed, right before ORDER BY, so the WHERE clause has no idea that the ranking column exists. It is only present after we have filtered and grouped all the rows.
To counteract this problem, we need to wrap our query into a derived table. We can then attach a where clause to it:
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ORDER BYLEAD - Window function to access a following row
This page is licensed: CC BY-SA / Gnu FDL
LAG (expr[, offset]) OVER (
[ PARTITION BY partition_expression ]
< ORDER BY order_list >
)Access data from a following row. This function returns the value of an expression from a row at a specified physical offset after the current row.
LEAD (expr[, offset]) OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)The LEAD function accesses data from a following row in the same result set without the need for a self-join. The specific row is determined by the offset (default 1), which specifies the number of rows ahead the current row to use. An offset of 0 is the current row.
- Window function to access a previous row
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Calculate the median value. This window function returns the middle value (50th percentile) of an ordered set of values within the window.
MEDIAN(median expression) OVER (
[ PARTITION BY partition_expression ]
)MEDIAN() is a that returns the median value of a range of values.
It is a specific case of , with an argument of 0.5 and the column the one in MEDIAN's argument.
Is equivalent to:
This page is licensed: CC BY-SA / Gnu FDL
function (expression) OVER (
[ PARTITION BY expression_list ]
[ ORDER BY order_list [ frame_clause ] ] )
function:
A valid window function
expression_list:
expression | column_name [, expr_list ]
order_list:
expression | column_name [ ASC | DESC ]
[, ... ]
frame_clause:
{ROWS | RANGE} {frame_border | BETWEEN frame_border AND frame_border}
frame_border:
| UNBOUNDED PRECEDING
| UNBOUNDED FOLLOWING
| CURRENT ROW
| expr PRECEDING
| expr FOLLOWINGCREATE TABLE student (name CHAR(10), test CHAR(10), score TINYINT);
INSERT INTO student VALUES
('Chun', 'SQL', 75), ('Chun', 'Tuning', 73),
('Esben', 'SQL', 43), ('Esben', 'Tuning', 31),
('Kaolin', 'SQL', 56), ('Kaolin', 'Tuning', 88),
('Tatiana', 'SQL', 87), ('Tatiana', 'Tuning', 83);SELECT name, test, score, AVG(score) OVER (PARTITION BY test)
AS average_by_test FROM student;
+---------+--------+-------+-----------------+
| name | test | score | average_by_test |
+---------+--------+-------+-----------------+
| Chun | SQL | 75 | 65.2500 |
| Chun | Tuning | 73 | 68.7500 |
| Esben | SQL | 43 | 65.2500 |
| Esben | Tuning | 31 | 68.7500 |
| Kaolin | SQL | 56 | 65.2500 |
| Kaolin | Tuning | 88 | 68.7500 |
| Tatiana | SQL | 87 | 65.2500 |
| Tatiana | Tuning | 83 | 68.7500 |
+---------+--------+-------+-----------------+
SELECT name, test, score, AVG(score) OVER (PARTITION BY name)
AS average_by_name FROM student;
+---------+--------+-------+-----------------+
| name | test | score | average_by_name |
+---------+--------+-------+-----------------+
| Chun | SQL | 75 | 74.0000 |
| Chun | Tuning | 73 | 74.0000 |
| Esben | SQL | 43 | 37.0000 |
| Esben | Tuning | 31 | 37.0000 |
| Kaolin | SQL | 56 | 72.0000 |
| Kaolin | Tuning | 88 | 72.0000 |
| Tatiana | SQL | 87 | 85.0000 |
| Tatiana | Tuning | 83 | 85.0000 |
+---------+--------+-------+-----------------+CREATE TABLE users (
email VARCHAR(30),
first_name VARCHAR(30),
last_name VARCHAR(30),
account_type VARCHAR(30)
);
INSERT INTO users VALUES
('admin@boss.org', 'Admin', 'Boss', 'admin'),
('bob.carlsen@foo.bar', 'Bob', 'Carlsen', 'regular'),
('eddie.stevens@data.org', 'Eddie', 'Stevens', 'regular'),
('john.smith@xyz.org', 'John', 'Smith', 'regular'),
('root@boss.org', 'Root', 'Chief', 'admin')SELECT row_number() OVER (ORDER BY email) AS rnum,
email, first_name, last_name, account_type
FROM users ORDER BY email;
+------+------------------------+------------+-----------+--------------+
| rnum | email | first_name | last_name | account_type |
+------+------------------------+------------+-----------+--------------+
| 1 | admin@boss.org | Admin | Boss | admin |
| 2 | bob.carlsen@foo.bar | Bob | Carlsen | regular |
| 3 | eddie.stevens@data.org | Eddie | Stevens | regular |
| 4 | john.smith@xyz.org | John | Smith | regular |
| 5 | root@boss.org | Root | Chief | admin |
+------+------------------------+------------+-----------+--------------SELECT row_number() OVER (PARTITION BY account_type ORDER BY email) AS rnum,
email, first_name, last_name, account_type
FROM users ORDER BY account_type,email;
+------+------------------------+------------+-----------+--------------+
| rnum | email | first_name | last_name | account_type |
+------+------------------------+------------+-----------+--------------+
| 1 | admin@boss.org | Admin | Boss | admin |
| 2 | root@boss.org | Root | Chief | admin |
| 1 | bob.carlsen@foo.bar | Bob | Carlsen | regular |
| 2 | eddie.stevens@data.org | Eddie | Stevens | regular |
| 3 | john.smith@xyz.org | John | Smith | regular |
+------+------------------------+------------+-----------+--------------+CREATE TABLE employee_salaries (dept VARCHAR(20), name VARCHAR(20), salary INT(11));
INSERT INTO employee_salaries VALUES
('Engineering', 'Dharma', 3500),
('Engineering', 'Binh', 3000),
('Engineering', 'Adalynn', 2800),
('Engineering', 'Samuel', 2500),
('Engineering', 'Cveta', 2200),
('Engineering', 'Ebele', 1800),
('Sales', 'Carbry', 500),
('Sales', 'Clytemnestra', 400),
('Sales', 'Juraj', 300),
('Sales', 'Kalpana', 300),
('Sales', 'Svantepolk', 250),
('Sales', 'Angelo', 200);select dept, name, salary
from employee_salaries as t1
where (select count(t2.salary)
from employee_salaries as t2
where t1.name != t2.name and
t1.dept = t2.dept and
t2.salary > t1.salary) < 5
order by dept, salary desc;
+-------------+--------------+--------+
| dept | name | salary |
+-------------+--------------+--------+
| Engineering | Dharma | 3500 |
| Engineering | Binh | 3000 |
| Engineering | Adalynn | 2800 |
| Engineering | Samuel | 2500 |
| Engineering | Cveta | 2200 |
| Sales | Carbry | 500 |
| Sales | Clytemnestra | 400 |
| Sales | Juraj | 300 |
| Sales | Kalpana | 300 |
| Sales | Svantepolk | 250 |
+-------------+--------------+--------+SELECT rank() OVER (PARTITION BY dept ORDER BY salary DESC) AS ranking,
dept, name, salary
FROM employee_salaries
ORDER BY dept, ranking;
+---------+-------------+--------------+--------+
| ranking | dept | name | salary |
+---------+-------------+--------------+--------+
| 1 | Engineering | Dharma | 3500 |
| 2 | Engineering | Binh | 3000 |
| 3 | Engineering | Adalynn | 2800 |
| 4 | Engineering | Samuel | 2500 |
| 5 | Engineering | Cveta | 2200 |
| 6 | Engineering | Ebele | 1800 |
| 1 | Sales | Carbry | 500 |
| 2 | Sales | Clytemnestra | 400 |
| 3 | Sales | Juraj | 300 |
| 3 | Sales | Kalpana | 300 |
| 5 | Sales | Svantepolk | 250 |
| 6 | Sales | Angelo | 200 |
+---------+-------------+--------------+--------+select
rank() over (partition by dept order by salary desc) as ranking,
dept, name, salary
from employee_salaries
where ranking <= 5
order by dept, ranking;
ERROR 1054 (42S22): Unknown column 'ranking' in 'where clause'select *from (select rank() over (partition by dept order by salary desc) as ranking,
dept, name, salary
from employee_salaries) as salary_ranks
where (salary_ranks.ranking <= 5)
order by dept, ranking;
+---------+-------------+--------------+--------+
| ranking | dept | name | salary |
+---------+-------------+--------------+--------+
| 1 | Engineering | Dharma | 3500 |
| 2 | Engineering | Binh | 3000 |
| 3 | Engineering | Adalynn | 2800 |
| 4 | Engineering | Samuel | 2500 |
| 5 | Engineering | Cveta | 2200 |
| 1 | Sales | Carbry | 500 |
| 2 | Sales | Clytemnestra | 400 |
| 3 | Sales | Juraj | 300 |
| 3 | Sales | Kalpana | 300 |
| 5 | Sales | Svantepolk | 250 |
+---------+-------------+--------------+--------+CREATE TABLE t1 (pk int primary key, a int, b int, c char(10), d decimal(10, 3), e real);
INSERT INTO t1 VALUES
( 1, 0, 1, 'one', 0.1, 0.001),
( 2, 0, 2, 'two', 0.2, 0.002),
( 3, 0, 3, 'three', 0.3, 0.003),
( 4, 1, 2, 'three', 0.4, 0.004),
( 5, 1, 1, 'two', 0.5, 0.005),
( 6, 1, 1, 'one', 0.6, 0.006),
( 7, 2, NULL, 'n_one', 0.5, 0.007),
( 8, 2, 1, 'n_two', NULL, 0.008),
( 9, 2, 2, NULL, 0.7, 0.009),
(10, 2, 0, 'n_four', 0.8, 0.010),
(11, 2, 10, NULL, 0.9, NULL);
SELECT pk, LAG(pk) OVER (ORDER BY pk) AS l,
LAG(pk,1) OVER (ORDER BY pk) AS l1,
LAG(pk,2) OVER (ORDER BY pk) AS l2,
LAG(pk,0) OVER (ORDER BY pk) AS l0,
LAG(pk,-1) OVER (ORDER BY pk) AS lm1,
LAG(pk,-2) OVER (ORDER BY pk) AS lm2
FROM t1;
+----+------+------+------+------+------+------+
| pk | l | l1 | l2 | l0 | lm1 | lm2 |
+----+------+------+------+------+------+------+
| 1 | NULL | NULL | NULL | 1 | 2 | 3 |
| 2 | 1 | 1 | NULL | 2 | 3 | 4 |
| 3 | 2 | 2 | 1 | 3 | 4 | 5 |
| 4 | 3 | 3 | 2 | 4 | 5 | 6 |
| 5 | 4 | 4 | 3 | 5 | 6 | 7 |
| 6 | 5 | 5 | 4 | 6 | 7 | 8 |
| 7 | 6 | 6 | 5 | 7 | 8 | 9 |
| 8 | 7 | 7 | 6 | 8 | 9 | 10 |
| 9 | 8 | 8 | 7 | 9 | 10 | 11 |
| 10 | 9 | 9 | 8 | 10 | 11 | NULL |
| 11 | 10 | 10 | 9 | 11 | NULL | NULL |
+----+------+------+------+------+------+------+CREATE TABLE student(course VARCHAR(10), mark int, name varchar(10));
INSERT INTO student VALUES
('Maths', 60, 'Thulile'),
('Maths', 60, 'Pritha'),
('Maths', 70, 'Voitto'),
('Maths', 55, 'Chun'),
('Biology', 60, 'Bilal'),
('Biology', 70, 'Roger');
SELECT
RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS rank,
DENSE_RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS dense_rank,
ROW_NUMBER() OVER (PARTITION BY course ORDER BY mark DESC) AS row_num,
course, mark, name
FROM student ORDER BY course, mark DESC;
+------+------------+---------+---------+------+---------+
| rank | dense_rank | row_num | course | mark | name |
+------+------------+---------+---------+------+---------+
| 1 | 1 | 1 | Biology | 70 | Roger |
| 2 | 2 | 2 | Biology | 60 | Bilal |
| 1 | 1 | 1 | Maths | 70 | Voitto |
| 2 | 2 | 2 | Maths | 60 | Thulile |
| 2 | 2 | 3 | Maths | 60 | Pritha |
| 4 | 3 | 4 | Maths | 55 | Chun |
+------+------------+---------+---------+------+---------+Essentially, the following process is followed to find the value to return:
Get the number of rows in the partition, denoted by N
RN = p*(N-1), where p denotes the argument to the PERCENTILE_CONT function
Calculate FRN as FRN=floor(RN) and CRN as CRN=ceil(RN)
Look up rows FRN and CRN
If (CRN = FRN = RN) then the result is (value of expression from row at RN)
Otherwise the result is
(CRN - RN) * (value of expression for row at FRN) +
(RN - FRN) * (value of expression for row at CRN)
The MEDIAN function is a specific case of PERCENTILE_CONT, equivalent to PERCENTILE_CONT(0.5).
MEDIAN() - a special case of PERCENTILE_CONT equivalent to PERCENTILE_CONT(0.5)
This page is licensed: CC BY-SA / Gnu FDL
The distinction between DENSE_RANK(), RANK() and ROW_NUMBER():
This page is licensed: CC BY-SA / Gnu FDL
This page is licensed: CC BY-SA / Gnu FDL
NTILE (expr) OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)CREATE TABLE t1 (pk int primary key, a int, b int, c char(10), d decimal(10, 3), e real);
INSERT INTO t1 VALUES
( 1, 0, 1, 'one', 0.1, 0.001),
( 2, 0, 2, 'two', 0.2, 0.002),
( 3, 0, 3, 'three', 0.3, 0.003),
( 4, 1, 2, 'three', 0.4, 0.004),
( 5, 1, 1, 'two', 0.5, 0.005),
( 6, 1, 1, 'one', 0.6, 0.006),
( 7, 2, NULL, 'n_one', 0.5, 0.007),
( 8, 2, 1, 'n_two', NULL, 0.008),
( 9, 2, 2, NULL, 0.7, 0.009),
(10, 2, 0, 'n_four', 0.8, 0.010),
(11, 2, 10, NULL, 0.9, NULL);
SELECT pk, LEAD(pk) OVER (ORDER BY pk) AS l,
LEAD(pk,1) OVER (ORDER BY pk) AS l1,
LEAD(pk,2) OVER (ORDER BY pk) AS l2,
LEAD(pk,0) OVER (ORDER BY pk) AS l0,
LEAD(pk,-1) OVER (ORDER BY pk) AS lm1,
LEAD(pk,-2) OVER (ORDER BY pk) AS lm2
FROM t1;
+----+------+------+------+------+------+------+
| pk | l | l1 | l2 | l0 | lm1 | lm2 |
+----+------+------+------+------+------+------+
| 1 | 2 | 2 | 3 | 1 | NULL | NULL |
| 2 | 3 | 3 | 4 | 2 | 1 | NULL |
| 3 | 4 | 4 | 5 | 3 | 2 | 1 |
| 4 | 5 | 5 | 6 | 4 | 3 | 2 |
| 5 | 6 | 6 | 7 | 5 | 4 | 3 |
| 6 | 7 | 7 | 8 | 6 | 5 | 4 |
| 7 | 8 | 8 | 9 | 7 | 6 | 5 |
| 8 | 9 | 9 | 10 | 8 | 7 | 6 |
| 9 | 10 | 10 | 11 | 9 | 8 | 7 |
| 10 | 11 | 11 | NULL | 10 | 9 | 8 |
| 11 | NULL | NULL | NULL | 11 | 10 | 9 |
+----+------+------+------+------+------+------+MEDIAN(<median-arg>) OVER ( [ PARTITION BY partition_expression] )PERCENTILE_CONT(0.5) WITHIN
GROUP (ORDER BY <median-arg>) OVER ( [ PARTITION BY partition_expression ])CREATE TABLE book_rating (name CHAR(30), star_rating TINYINT);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 5);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 3);
INSERT INTO book_rating VALUES ('Lady of the Flies', 1);
INSERT INTO book_rating VALUES ('Lady of the Flies', 2);
INSERT INTO book_rating VALUES ('Lady of the Flies', 5);
SELECT name, median(star_rating) OVER (PARTITION BY name) FROM book_rating;
+-----------------------+----------------------------------------------+
| name | median(star_rating) OVER (PARTITION BY name) |
+-----------------------+----------------------------------------------+
| Lord of the Ladybirds | 4.0000000000 |
| Lord of the Ladybirds | 4.0000000000 |
| Lady of the Flies | 2.0000000000 |
| Lady of the Flies | 2.0000000000 |
| Lady of the Flies | 2.0000000000 |
+-----------------------+----------------------------------------------+CREATE TABLE book_rating (name CHAR(30), star_rating TINYINT);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 5);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 3);
INSERT INTO book_rating VALUES ('Lady of the Flies', 1);
INSERT INTO book_rating VALUES ('Lady of the Flies', 2);
INSERT INTO book_rating VALUES ('Lady of the Flies', 5);
SELECT name, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc
FROM book_rating;
+-----------------------+--------------+
| name | pc |
+-----------------------+--------------+
| Lord of the Ladybirds | 4.0000000000 |
| Lord of the Ladybirds | 4.0000000000 |
| Lady of the Flies | 2.0000000000 |
| Lady of the Flies | 2.0000000000 |
| Lady of the Flies | 2.0000000000 |
+-----------------------+--------------+
SELECT name, PERCENTILE_CONT(1) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc
FROM book_rating;
+-----------------------+--------------+
| name | pc |
+-----------------------+--------------+
| Lord of the Ladybirds | 5.0000000000 |
| Lord of the Ladybirds | 5.0000000000 |
| Lady of the Flies | 5.0000000000 |
| Lady of the Flies | 5.0000000000 |
| Lady of the Flies | 5.0000000000 |
+-----------------------+--------------+
SELECT name, PERCENTILE_CONT(0) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc
FROM book_rating;
+-----------------------+--------------+
| name | pc |
+-----------------------+--------------+
| Lord of the Ladybirds | 3.0000000000 |
| Lord of the Ladybirds | 3.0000000000 |
| Lady of the Flies | 1.0000000000 |
| Lady of the Flies | 1.0000000000 |
| Lady of the Flies | 1.0000000000 |
+-----------------------+--------------+
SELECT name, PERCENTILE_CONT(0.6) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc
FROM book_rating;
+-----------------------+--------------+
| name | pc |
+-----------------------+--------------+
| Lord of the Ladybirds | 4.2000000000 |
| Lord of the Ladybirds | 4.2000000000 |
| Lady of the Flies | 2.6000000000 |
| Lady of the Flies | 2.6000000000 |
| Lady of the Flies | 2.6000000000 |
+-----------------------+--------------+DENSE_RANK() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)CREATE TABLE student(course VARCHAR(10), mark int, name varchar(10));
INSERT INTO student VALUES
('Maths', 60, 'Thulile'),
('Maths', 60, 'Pritha'),
('Maths', 70, 'Voitto'),
('Maths', 55, 'Chun'),
('Biology', 60, 'Bilal'),
('Biology', 70, 'Roger');
SELECT
RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS rank,
DENSE_RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS dense_rank,
ROW_NUMBER() OVER (PARTITION BY course ORDER BY mark DESC) AS row_num,
course, mark, name
FROM student ORDER BY course, mark DESC;
+------+------------+---------+---------+------+---------+
| rank | dense_rank | row_num | course | mark | name |
+------+------------+---------+---------+------+---------+
| 1 | 1 | 1 | Biology | 70 | Roger |
| 2 | 2 | 2 | Biology | 60 | Bilal |
| 1 | 1 | 1 | Maths | 70 | Voitto |
| 2 | 2 | 2 | Maths | 60 | Thulile |
| 2 | 2 | 3 | Maths | 60 | Pritha |
| 4 | 3 | 4 | Maths | 55 | Chun |
+------+------------+---------+---------+------+---------+create table t1 (
pk int primary key,
a int,
b int
);
insert into t1 values
(11 , 0, 10),
(12 , 0, 10),
(13 , 1, 10),
(14 , 1, 10),
(18 , 2, 10),
(15 , 2, 20),
(16 , 2, 20),
(17 , 2, 20),
(19 , 4, 20),
(20 , 4, 20);
select pk, a, b,
ntile(1) over (order by pk)
from t1;
+----+------+------+-----------------------------+
| pk | a | b | ntile(1) over (order by pk) |
+----+------+------+-----------------------------+
| 11 | 0 | 10 | 1 |
| 12 | 0 | 10 | 1 |
| 13 | 1 | 10 | 1 |
| 14 | 1 | 10 | 1 |
| 15 | 2 | 20 | 1 |
| 16 | 2 | 20 | 1 |
| 17 | 2 | 20 | 1 |
| 18 | 2 | 10 | 1 |
| 19 | 4 | 20 | 1 |
| 20 | 4 | 20 | 1 |
+----+------+------+-----------------------------+
select pk, a, b,
ntile(4) over (order by pk)
from t1;
+----+------+------+-----------------------------+
| pk | a | b | ntile(4) over (order by pk) |
+----+------+------+-----------------------------+
| 11 | 0 | 10 | 1 |
| 12 | 0 | 10 | 1 |
| 13 | 1 | 10 | 1 |
| 14 | 1 | 10 | 2 |
| 15 | 2 | 20 | 2 |
| 16 | 2 | 20 | 2 |
| 17 | 2 | 20 | 3 |
| 18 | 2 | 10 | 3 |
| 19 | 4 | 20 | 4 |
| 20 | 4 | 20 | 4 |
+----+------+------+-----------------------------+Calculate a discrete percentile. This inverse distribution function returns an actual value from the dataset that corresponds to the specified percentile.
PERCENTILE_DISC() (standing for discrete percentile) is a which returns the first value in the set whose ordered position is the same or more than the specified fraction.
Essentially, the following process is followed to find the value to return:
Get the number of rows in the partition.
Walk through the partition, in order, until finding the first row with >= function_argument.
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Calculate relative rank. This function returns the percentage rank of the current row within its partition, ranging from 0 to 1.
Calculate rank with gaps. This function assigns a rank to each row, with tied values receiving the same rank and subsequent ranks skipped.
(rank - 1) / (number of rows in the window or partition - 1)This page is licensed: CC BY-SA / Gnu FDL
FIRST_VALUE(expr) OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)This page is licensed: CC BY-SA / Gnu FDL
CUME_DIST() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)The distinction between DENSE_RANK(), RANK() and ROW_NUMBER():
This page is licensed: CC BY-SA / Gnu FDL
CREATE TABLE book_rating (name CHAR(30), star_rating TINYINT);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 5);
INSERT INTO book_rating VALUES ('Lord of the Ladybirds', 3);
INSERT INTO book_rating VALUES ('Lady of the Flies', 1);
INSERT INTO book_rating VALUES ('Lady of the Flies', 2);
INSERT INTO book_rating VALUES ('Lady of the Flies', 5);
SELECT name, PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc FROM book_rating;
+-----------------------+------+
| name | pc |
+-----------------------+------+
| Lord of the Ladybirds | 3 |
| Lord of the Ladybirds | 3 |
| Lady of the Flies | 2 |
| Lady of the Flies | 2 |
| Lady of the Flies | 2 |
+-----------------------+------+
5 rows in set (0.000 sec)
SELECT name, PERCENTILE_DISC(0) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc FROM book_rating;
+-----------------------+------+
| name | pc |
+-----------------------+------+
| Lord of the Ladybirds | 3 |
| Lord of the Ladybirds | 3 |
| Lady of the Flies | 1 |
| Lady of the Flies | 1 |
| Lady of the Flies | 1 |
+-----------------------+------+
5 rows in set (0.000 sec)
SELECT name, PERCENTILE_DISC(1) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc FROM book_rating;
+-----------------------+------+
| name | pc |
+-----------------------+------+
| Lord of the Ladybirds | 5 |
| Lord of the Ladybirds | 5 |
| Lady of the Flies | 5 |
| Lady of the Flies | 5 |
| Lady of the Flies | 5 |
+-----------------------+------+
5 rows in set (0.000 sec)
SELECT name, PERCENTILE_DISC(0.6) WITHIN GROUP (ORDER BY star_rating)
OVER (PARTITION BY name) AS pc FROM book_rating;
+-----------------------+------+
| name | pc |
+-----------------------+------+
| Lord of the Ladybirds | 5 |
| Lord of the Ladybirds | 5 |
| Lady of the Flies | 2 |
| Lady of the Flies | 2 |
| Lady of the Flies | 2 |
+-----------------------+------create table t1 (
pk int primary key,
a int,
b int
);
insert into t1 values
( 1 , 0, 10),
( 2 , 0, 10),
( 3 , 1, 10),
( 4 , 1, 10),
( 8 , 2, 10),
( 5 , 2, 20),
( 6 , 2, 20),
( 7 , 2, 20),
( 9 , 4, 20),
(10 , 4, 20);
select pk, a, b,
rank() over (order by a) as rank,
percent_rank() over (order by a) as pct_rank,
cume_dist() over (order by a) as cume_dist
from t1;
+----+------+------+------+--------------+--------------+
| pk | a | b | rank | pct_rank | cume_dist |
+----+------+------+------+--------------+--------------+
| 1 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 2 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 3 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 4 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 5 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 6 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 7 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 8 | 2 | 10 | 5 | 0.4444444444 | 0.8000000000 |
| 9 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
| 10 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
+----+------+------+------+--------------+--------------+
select pk, a, b,
percent_rank() over (order by pk) as pct_rank,
cume_dist() over (order by pk) as cume_dist
from t1 order by pk;
+----+------+------+--------------+--------------+
| pk | a | b | pct_rank | cume_dist |
+----+------+------+--------------+--------------+
| 1 | 0 | 10 | 0.0000000000 | 0.1000000000 |
| 2 | 0 | 10 | 0.1111111111 | 0.2000000000 |
| 3 | 1 | 10 | 0.2222222222 | 0.3000000000 |
| 4 | 1 | 10 | 0.3333333333 | 0.4000000000 |
| 5 | 2 | 20 | 0.4444444444 | 0.5000000000 |
| 6 | 2 | 20 | 0.5555555556 | 0.6000000000 |
| 7 | 2 | 20 | 0.6666666667 | 0.7000000000 |
| 8 | 2 | 10 | 0.7777777778 | 0.8000000000 |
| 9 | 4 | 20 | 0.8888888889 | 0.9000000000 |
| 10 | 4 | 20 | 1.0000000000 | 1.0000000000 |
+----+------+------+--------------+--------------+
select pk, a, b,
percent_rank() over (partition by a order by a) as pct_rank,
cume_dist() over (partition by a order by a) as cume_dist
from t1;
+----+------+------+--------------+--------------+
| pk | a | b | pct_rank | cume_dist |
+----+------+------+--------------+--------------+
| 1 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 2 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 3 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 4 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 5 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 6 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 7 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 8 | 2 | 10 | 0.0000000000 | 1.0000000000 |
| 9 | 4 | 20 | 0.0000000000 | 1.0000000000 |
| 10 | 4 | 20 | 0.0000000000 | 1.0000000000 |
+----+------+------+--------------+--------------+CREATE TABLE t1 (
pk int primary key,
a int,
b int,
c char(10),
d decimal(10, 3),
e real
);
INSERT INTO t1 VALUES
( 1, 0, 1, 'one', 0.1, 0.001),
( 2, 0, 2, 'two', 0.2, 0.002),
( 3, 0, 3, 'three', 0.3, 0.003),
( 4, 1, 2, 'three', 0.4, 0.004),
( 5, 1, 1, 'two', 0.5, 0.005),
( 6, 1, 1, 'one', 0.6, 0.006),
( 7, 2, NULL, 'n_one', 0.5, 0.007),
( 8, 2, 1, 'n_two', NULL, 0.008),
( 9, 2, 2, NULL, 0.7, 0.009),
(10, 2, 0, 'n_four', 0.8, 0.010),
(11, 2, 10, NULL, 0.9, NULL);
SELECT pk, FIRST_VALUE(pk) OVER (ORDER BY pk) AS first_asc,
LAST_VALUE(pk) OVER (ORDER BY pk) AS last_asc,
FIRST_VALUE(pk) OVER (ORDER BY pk DESC) AS first_desc,
LAST_VALUE(pk) OVER (ORDER BY pk DESC) AS last_desc
FROM t1
ORDER BY pk DESC;
+----+-----------+----------+------------+-----------+
| pk | first_asc | last_asc | first_desc | last_desc |
+----+-----------+----------+------------+-----------+
| 11 | 1 | 11 | 11 | 11 |
| 10 | 1 | 10 | 11 | 10 |
| 9 | 1 | 9 | 11 | 9 |
| 8 | 1 | 8 | 11 | 8 |
| 7 | 1 | 7 | 11 | 7 |
| 6 | 1 | 6 | 11 | 6 |
| 5 | 1 | 5 | 11 | 5 |
| 4 | 1 | 4 | 11 | 4 |
| 3 | 1 | 3 | 11 | 3 |
| 2 | 1 | 2 | 11 | 2 |
| 1 | 1 | 1 | 11 | 1 |
+----+-----------+----------+------------+-----------+CREATE OR REPLACE TABLE t1 (i int);
INSERT INTO t1 VALUES (1),(2),(3),(4),(5),(6),(7),(8),(9),(10);
SELECT i,
FIRST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW and 1 FOLLOWING) AS f_1f,
LAST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN CURRENT ROW and 1 FOLLOWING) AS l_1f,
FIRST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS f_1p1f,
LAST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS l_1p1f,
FIRST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 2 PRECEDING AND 1 PRECEDING) AS f_2p1p,
LAST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 2 PRECEDING AND 1 PRECEDING) AS l_2p1p,
FIRST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 1 FOLLOWING AND 2 FOLLOWING) AS f_1f2f,
LAST_VALUE(i) OVER (ORDER BY i ROWS BETWEEN 1 FOLLOWING AND 2 FOLLOWING) AS l_1f2f
FROM t1;
+------+------+------+--------+--------+--------+--------+--------+--------+
| i | f_1f | l_1f | f_1p1f | l_1p1f | f_2p1p | l_2p1p | f_1f2f | l_1f2f |
+------+------+------+--------+--------+--------+--------+--------+--------+
| 1 | 1 | 2 | 1 | 2 | NULL | NULL | 2 | 3 |
| 2 | 2 | 3 | 1 | 3 | 1 | 1 | 3 | 4 |
| 3 | 3 | 4 | 2 | 4 | 1 | 2 | 4 | 5 |
| 4 | 4 | 5 | 3 | 5 | 2 | 3 | 5 | 6 |
| 5 | 5 | 6 | 4 | 6 | 3 | 4 | 6 | 7 |
| 6 | 6 | 7 | 5 | 7 | 4 | 5 | 7 | 8 |
| 7 | 7 | 8 | 6 | 8 | 5 | 6 | 8 | 9 |
| 8 | 8 | 9 | 7 | 9 | 6 | 7 | 9 | 10 |
| 9 | 9 | 10 | 8 | 10 | 7 | 8 | 10 | 10 |
| 10 | 10 | 10 | 9 | 10 | 8 | 9 | NULL | NULL |
+------+------+------+--------+--------+--------+--------+--------+--------+(number of rows <= current row) / (total rows)create table t1 (
pk int primary key,
a int,
b int
);
insert into t1 values
( 1 , 0, 10),
( 2 , 0, 10),
( 3 , 1, 10),
( 4 , 1, 10),
( 8 , 2, 10),
( 5 , 2, 20),
( 6 , 2, 20),
( 7 , 2, 20),
( 9 , 4, 20),
(10 , 4, 20);
select pk, a, b,
rank() over (order by a) as rank,
percent_rank() over (order by a) as pct_rank,
cume_dist() over (order by a) as cume_dist
from t1;
+----+------+------+------+--------------+--------------+
| pk | a | b | rank | pct_rank | cume_dist |
+----+------+------+------+--------------+--------------+
| 1 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 2 | 0 | 10 | 1 | 0.0000000000 | 0.2000000000 |
| 3 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 4 | 1 | 10 | 3 | 0.2222222222 | 0.4000000000 |
| 5 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 6 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 7 | 2 | 20 | 5 | 0.4444444444 | 0.8000000000 |
| 8 | 2 | 10 | 5 | 0.4444444444 | 0.8000000000 |
| 9 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
| 10 | 4 | 20 | 9 | 0.8888888889 | 1.0000000000 |
+----+------+------+------+--------------+--------------+
select pk, a, b,
percent_rank() over (order by pk) as pct_rank,
cume_dist() over (order by pk) as cume_dist
from t1 order by pk;
+----+------+------+--------------+--------------+
| pk | a | b | pct_rank | cume_dist |
+----+------+------+--------------+--------------+
| 1 | 0 | 10 | 0.0000000000 | 0.1000000000 |
| 2 | 0 | 10 | 0.1111111111 | 0.2000000000 |
| 3 | 1 | 10 | 0.2222222222 | 0.3000000000 |
| 4 | 1 | 10 | 0.3333333333 | 0.4000000000 |
| 5 | 2 | 20 | 0.4444444444 | 0.5000000000 |
| 6 | 2 | 20 | 0.5555555556 | 0.6000000000 |
| 7 | 2 | 20 | 0.6666666667 | 0.7000000000 |
| 8 | 2 | 10 | 0.7777777778 | 0.8000000000 |
| 9 | 4 | 20 | 0.8888888889 | 0.9000000000 |
| 10 | 4 | 20 | 1.0000000000 | 1.0000000000 |
+----+------+------+--------------+--------------+
select pk, a, b,
percent_rank() over (partition by a order by a) as pct_rank,
cume_dist() over (partition by a order by a) as cume_dist
from t1;
+----+------+------+--------------+--------------+
| pk | a | b | pct_rank | cume_dist |
+----+------+------+--------------+--------------+
| 1 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 2 | 0 | 10 | 0.0000000000 | 1.0000000000 |
| 3 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 4 | 1 | 10 | 0.0000000000 | 1.0000000000 |
| 5 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 6 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 7 | 2 | 20 | 0.0000000000 | 1.0000000000 |
| 8 | 2 | 10 | 0.0000000000 | 1.0000000000 |
| 9 | 4 | 20 | 0.0000000000 | 1.0000000000 |
| 10 | 4 | 20 | 0.0000000000 | 1.0000000000 |
+----+------+------+--------------+--------------+RANK() OVER (
[ PARTITION BY partition_expression ]
[ ORDER BY order_list ]
)CREATE TABLE student(course VARCHAR(10), mark int, name varchar(10));
INSERT INTO student VALUES
('Maths', 60, 'Thulile'),
('Maths', 60, 'Pritha'),
('Maths', 70, 'Voitto'),
('Maths', 55, 'Chun'),
('Biology', 60, 'Bilal'),
('Biology', 70, 'Roger');
SELECT
RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS rank,
DENSE_RANK() OVER (PARTITION BY course ORDER BY mark DESC) AS dense_rank,
ROW_NUMBER() OVER (PARTITION BY course ORDER BY mark DESC) AS row_num,
course, mark, name
FROM student ORDER BY course, mark DESC;
+------+------------+---------+---------+------+---------+
| rank | dense_rank | row_num | course | mark | name |
+------+------------+---------+---------+------+---------+
| 1 | 1 | 1 | Biology | 70 | Roger |
| 2 | 2 | 2 | Biology | 60 | Bilal |
| 1 | 1 | 1 | Maths | 70 | Voitto |
| 2 | 2 | 2 | Maths | 60 | Thulile |
| 2 | 2 | 3 | Maths | 60 | Pritha |
| 4 | 3 | 4 | Maths | 55 | Chun |
+------+------------+---------+---------+------+---------+Define subsets of rows for calculation. Window frames specify which rows relative to the current row are included in the window function's calculation.
A basic overview of window functions is described in Window Functions Overview. Window frames expand this functionality by allowing the function to include a specified a number of rows around the current row.
These include:
All rows before the current row (UNBOUNDED PRECEDING), for example RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW .
All rows after the current row (UNBOUNDED FOLLOWING), for example RANGE BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING .
A set number of rows before the current row (expr PRECEDING) for example RANGE BETWEEN 6 PRECEDING AND CURRENT ROW .
The following functions operate on window frames:
Window frames are determined by the frame_clause in the window function request.
Take the following example:
By not specifying an OVER clause, the function is run over the entire dataset. However, if we specify an ORDER BY condition based on score (and order the entire result in the same way for clarity), the following result is returned:
The total_score column represents a running total of the current row, and all previous rows. The window frame in this example expands as the function proceeds.
The above query makes use of the default to define the window frame. It could be written explicitly as follows:
Let's look at some alternatives:
Firstly, applying the window function to the current row and all following rows can be done with the use of UNBOUNDED FOLLOWING:
It's possible to specify a number of rows, rather than the entire unbounded following or preceding set. The following example takes the current row, as well as the previous row:
The current row and the following row:
This page is licensed: CC BY-SA / Gnu FDL
frame_clause:
{ROWS | RANGE} {frame_border | BETWEEN frame_border AND frame_border}
frame_border:
| UNBOUNDED PRECEDING
| UNBOUNDED FOLLOWING
| CURRENT ROW
| expr PRECEDING
| expr FOLLOWINGA set number of rows after the current row (expr PRECEDING AND expr FOLLOWING) for example RANGE BETWEEN CURRENT ROW AND 2 FOLLOWING .
A specified number of rows both before and after the current row, for example RANGE BETWEEN 6 PRECEDING AND 3 FOLLOWING .
CREATE TABLE `student_test` (
name char(10),
test char(10),
score tinyint(4)
);
INSERT INTO student_test VALUES
('Chun', 'SQL', 75), ('Chun', 'Tuning', 73),
('Esben', 'SQL', 43), ('Esben', 'Tuning', 31),
('Kaolin', 'SQL', 56), ('Kaolin', 'Tuning', 88),
('Tatiana', 'SQL', 87);
SELECT name, test, score, SUM(score)
OVER () AS total_score
FROM student_test;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Chun | SQL | 75 | 453 |
| Chun | Tuning | 73 | 453 |
| Esben | SQL | 43 | 453 |
| Esben | Tuning | 31 | 453 |
| Kaolin | SQL | 56 | 453 |
| Kaolin | Tuning | 88 | 453 |
| Tatiana | SQL | 87 | 453 |
+---------+--------+-------+-------------+SELECT name, test, score, SUM(score)
OVER (ORDER BY score) AS total_score
FROM student_test ORDER BY score;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Esben | Tuning | 31 | 31 |
| Esben | SQL | 43 | 74 |
| Kaolin | SQL | 56 | 130 |
| Chun | Tuning | 73 | 203 |
| Chun | SQL | 75 | 278 |
| Tatiana | SQL | 87 | 365 |
| Kaolin | Tuning | 88 | 453 |
+---------+--------+-------+-------------+SELECT name, test, score, SUM(score)
OVER (ORDER BY score RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS total_score
FROM student_test ORDER BY score;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Esben | Tuning | 31 | 31 |
| Esben | SQL | 43 | 74 |
| Kaolin | SQL | 56 | 130 |
| Chun | Tuning | 73 | 203 |
| Chun | SQL | 75 | 278 |
| Tatiana | SQL | 87 | 365 |
| Kaolin | Tuning | 88 | 453 |
+---------+--------+-------+-------------+SELECT name, test, score, SUM(score)
OVER (ORDER BY score RANGE BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS total_score
FROM student_test ORDER BY score;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Esben | Tuning | 31 | 453 |
| Esben | SQL | 43 | 422 |
| Kaolin | SQL | 56 | 379 |
| Chun | Tuning | 73 | 323 |
| Chun | SQL | 75 | 250 |
| Tatiana | SQL | 87 | 175 |
| Kaolin | Tuning | 88 | 88 |
+---------+--------+-------+-------------+SELECT name, test, score, SUM(score)
OVER (ORDER BY score ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS total_score
FROM student_test ORDER BY score;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Esben | Tuning | 31 | 31 |
| Esben | SQL | 43 | 74 |
| Kaolin | SQL | 56 | 99 |
| Chun | Tuning | 73 | 129 |
| Chun | SQL | 75 | 148 |
| Tatiana | SQL | 87 | 162 |
| Kaolin | Tuning | 88 | 175 |
+---------+--------+-------+-------------+SELECT name, test, score, SUM(score)
OVER (ORDER BY score ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) AS total_score
FROM student_test ORDER BY score;
+---------+--------+-------+-------------+
| name | test | score | total_score |
+---------+--------+-------+-------------+
| Esben | Tuning | 31 | 74 |
| Esben | SQL | 43 | 130 |
| Kaolin | SQL | 56 | 172 |
| Chun | Tuning | 73 | 204 |
| Chun | SQL | 75 | 235 |
| Tatiana | SQL | 87 | 250 |
| Kaolin | Tuning | 88 | 175 |
+---------+--------+-------+-------------+Review window function support in ColumnStore. This page details the specific window functions and limitations applicable when using the ColumnStore storage engine.
MariaDB ColumnStore provides support for window functions broadly following the SQL 2003 specification. A window function allows for calculations relating to a window of data surrounding the current row in a result set. This capability provides for simplified queries in support of common business questions such as cumulative totals, rolling averages, and top 10 lists.
Aggregate functions are utilized for window functions however differ in behavior from a group by query because the rows remain ungrouped. This provides support for cumulative sums and rolling averages, for example.
Two key concepts for window functions are Partition and Frame:
A Partition is a group of rows, or window, that have the same value for a specific column, for example a Partition can be created over a time period such as a quarter or lookup values.
The Frame for each row is a subset of the row's Partition. The frame typically is dynamic allowing for a sliding frame of rows within the Partition. The Frame determines the range of rows for the windowing function. A Frame could be defined as the last X rows and next Y rows all the way up to the entire Partition.
Window functions are applied after joins, group by, and having clauses are calculated.
A window function is applied in the select clause using the following syntax:
where window_definition is defined as:
PARTITION BY:
Divides the window result set into groups based on one or more expressions.
An expression may be a constant, column, and non window function expressions.
A query is not limited to a single partition by clause. Different partition clauses can be used across different window function applications.
The partition by columns do not need to be in the select list but do need to be available from the query result set.
ORDER BY:
Defines the ordering of values within the partition.
Can be ordered by multiple keys which may be a constant, column or non window function expression.
The order by columns do not need to be in the select list but need to be available from the query result set.
Use of a select column alias from the query is not supported.
and the optional frame_clause is defined as:
and the optional frame_start and frame_end are defined as (value being a numeric expression):
RANGE/ROWS:
Defines the windowing clause for calculating the set of rows that the function applies to for calculating a given rows window function result.
Requires an ORDER BY clause to define the row order for the window.
ROWS specify the window in physical units, i.e. result set rows and must be a constant or expression evaluating to a positive numeric value.
The examples are all based on the following simplified sales opportunity table:
Some example values are (thanks to for sample data generation):
The schema, sample data, and queries are available as an attachment to this article.
Window functions can be used to achieve cumulative / running calculations on a detail report. In this case a won opportunity report for a 7 day period adds columns to show the accumulated won amount as well as the current highest opportunity amount in preceding rows.
with example results:
The above example can be partitioned, so that the window functions are over a particular field grouping such as owner and accumulate within that grouping. This is achieved by adding the syntax "partition by" in the window function clause.
With example results:
The rank window function allows for ranking or assigning a numeric order value based on the window function definition. Using the Rank() function will result in the same value for ties / equal values and the next rank value skipped. The Dense_Rank() function behaves similarly except the next consecutive number is used after a tie rather than skipped. The Row_Number() function will provide a unique ordering value. The example query shows the Rank() function being applied to rank sales reps by the number of opportunities for Q4 2016.
with example results (note the query is technically incorrect by using closeDate < '2016-12-31' however this creates a tie scenario for illustrative purposes):
If the dense_rank function is used the rank values would be 1,2,3,3,4 and for the row_number function the values would be 1,2,3,4,5.
The first_value and last_value functions allow determining the first and last values of a given range. Combined with a group by this allows summarizing opening and closing values. The example shows a more complex case where detailed information is presented for first and last opportunity by quarter.
with example results:
Sometimes it useful to understand the previous and next values in the context of a given row. The lag and lead window functions provide this capability. By default the offset is one providing the prior or next value but can also be provided to get a larger offset. The example query is a report of opportunities by account name showing the opportunity amount, and the prior and next opportunity amount for that account by close date.
With example results:
The NTile window function allows for breaking up a data set into portions assigned a numeric value to each portion of the range. NTile(4) breaks the data up into quartiles (4 sets). The example query produces a report of all opportunities summarizing the quartile boundaries of amount values.
With example results:
The percentile functions have a slightly different syntax from other window functions as can be seen in the example below. These functions can be only applied against numeric values. The argument to the function is the percentile to evaluate. Following 'within group' is the sort expression which indicates the sort column and optionally order. Finally after 'over' is an optional partition by clause, for no partition clause use 'over ()'. The example below utilizes the value 0.5 to calculate the median opportunity amount in the rows. The values differ sometimes because percentile_cont will return the average of the 2 middle rows for an even data set while percentile_desc returns the first encountered in the sort.
With example results:
This page is licensed: CC BY-SA / Gnu FDL
If there is no PARTITION BY clause, all rows of the result set define the group.
ASC (default) and DESC options allow for ordering ascending or descending.
NULLS FIRST and NULL_LAST options specify whether null values come first or last in the ordering sequence. NULLS_FIRST is the default for ASC order, and NULLS_LAST is the default for DESC order.
RANGE specifies the window as a logical offset. If the expression evaluates to a numeric value then the ORDER BY expression must be a numeric or DATE type. If the expression evaluates to an interval value then the ORDER BY expression must be a DATE data type.UNBOUNDED PRECEDING indicates the window starts at the first row of the partition.
UNBOUNDED FOLLOWING indicates the window ends at the last row of the partition.
CURRENT ROW specifies the window start or ends at the current row or value.
If omitted, the default is ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
LAST_VALUE()
The value evaluated at the row that is the last row of the window frame (counting from 1); null if no such row.
LEAD()
Provides access to a row at a given physical offset beyond that position. Returns value evaluated at the row that is offset rows after the current row within the partition; if there is no such row, instead return default. Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to null.
MAX()
Maximum value of expression across all input values.
MEDIAN()
An inverse distribution function that assumes a continuous distribution model. It takes a numeric or datetime value and returns the middle value or an interpolated value that would be the middle value once the values are sorted. Nulls are ignored in the calculation.
MIN()
Minimum value of expression across all input values.
NTH_VALUE()
The value evaluated at the row that is the nth row of the window frame (counting from 1); null if no such row.
NTILE()
Divides an ordered data set into a number of buckets indicated by expr and assigns the appropriate bucket number to each row. The buckets are numbered 1 through expr. The expr value must resolve to a positive constant for each partition. Integer ranging from 1 to the argument value, dividing the partition as equally as possible.
PERCENT_RANK()
relative rank of the current row: (rank - 1) / (total rows - 1).
PERCENTILE_CONT()
An inverse distribution function that assumes a continuous distribution model. It takes a percentile value and a sort specification, and returns an interpolated value that would fall into that percentile value with respect to the sort specification. Nulls are ignored in the calculation.
PERCENTILE_DISC()
An inverse distribution function that assumes a discrete distribution model. It takes a percentile value and a sort specification and returns an element from the set. Nulls are ignored in the calculation.
RANK()
rank of the current row with gaps; same as row_number of its first peer.
ROW_NUMBER()
number of the current row within its partition, counting from 1
STDDEV() STDDEV_POP()
Computes the population standard deviation and returns the square root of the population variance.
STDDEV_SAMP()
Computes the cumulative sample standard deviation and returns the square root of the sample variance.
SUM()
Sum of expression across all input values.
VARIANCE() VAR_POP()
Population variance of the input values (square of the population standard deviation).
VAR_SAMP()
Sample variance of the input values (square of the sample standard deviation).
Organic demand-driven benchmark
Maria
477878.41
2016-11-28
ClosedWon
3
Miboo
De-engineered hybrid groupware
Olivier
80181.78
2017-01-05
ClosedWon
4
Youbridge
Enterprise-wide bottom-line Graphic Interface
Chris
946245.29
2016-07-02
ClosedWon
5
Skyba
Reverse-engineered fresh-thinking standardization
Maria
696241.82
2017-02-17
Negotiating
6
Eayo
Fundamental well-modulated artificial intelligence
Bob
765605.52
2016-08-27
Prospecting
7
Yotz
Extended secondary infrastructure
Chris
319624.20
2017-01-06
ClosedLost
8
Oloo
Configurable web-enabled data-warehouse
Chris
321016.26
2017-03-08
ClosedLost
9
Kaymbo
Multi-lateral web-enabled definition
Bob
690881.01
2017-01-02
Developing
10
Rhyloo
Public-key coherent infrastructure
Chris
965477.74
2016-11-07
Prospecting
583722.98
437636.47
Olivier
Devpulse
2016-10-05
834235.93
1417958.91
834235.93
Chris
Linkbridge
2016-10-07
539977.45
2458738.65
834235.93
Olivier
Trupe
2016-10-07
500802.29
1918761.20
834235.93
Bill
Latz
2016-10-08
857254.87
3315993.52
857254.87
Chris
Avamm
2016-10-09
699566.86
4015560.38
857254.87
583722.98
437636.47
Bill
Latz
2016-10-08
857254.87
1440977.85
857254.87
Chris
Linkbridge
2016-10-07
539977.45
539977.45
539977.45
Chris
Avamm
2016-10-09
699566.86
1239544.31
699566.86
Olivier
Devpulse
2016-10-05
834235.93
834235.93
834235.93
Olivier
Trupe
2016-10-07
500802.29
1335038.22
834235.93
Olivier
10
5
2016
4
Skimia
Chris
961513.59
Avamm
Maria
112493.65
2017
1
Yombu
Bob
536875.51
Skaboo
Chris
270273.08
2016-12-18
350235.75
161086.82
878595.89
Abata
2016-12-31
878595.89
350235.75
922322.39
Abata
2017-01-21
922322.39
878595.89
NULL
Abatz
2016-10-19
795424.15
NULL
NULL
Agimba
2016-07-09
288974.84
NULL
914461.49
Agimba
2016-09-07
914461.49
288974.84
176645.52
Agimba
2016-09-20
176645.52
914461.49
NULL
437636.4700000000
437636.47
Bill
Latz
2016-10-08
857254.87
437636.4700000000
437636.47
Chris
Linkbridge
2016-10-07
539977.45
619772.1550000000
539977.45
Chris
Avamm
2016-10-09
699566.86
619772.1550000000
539977.45
Olivier
Devpulse
2016-10-05
834235.93
667519.1100000000
500802.29
Olivier
Trupe
2016-10-07
500802.29
667519.1100000000
500802.29
AVG()
The average of all input values.
COUNT()
Number of input rows.
CUME_DIST()
Calculates the cumulative distribution, or relative rank, of the current row to other rows in the same partition. Number of peer or preceding rows / number of rows in partition.
DENSE_RANK()
Ranks items in a group leaving no gaps in ranking sequence when there are ties.
FIRST_VALUE()
The value evaluated at the row that is the first row of the window frame (counting from 1); null if no such row.
LAG()
The value evaluated at the row that is offset rows before the current row within the partition; if there is no such row, instead return default. Both offset and default are evaluated with respect to the current row. If omitted, offset defaults to 1 and default to null. LAG provides access to more than one row of a table at the same time without a self-join. Given a series of rows returned from a query and a position of the cursor, LAG provides access to a row at a given physical offset prior to that position.
1
Browseblab
Multi-lateral executive function
Bob
26444.86
2016-10-20
Negotiating
2
Bill
Babbleopia
2016-10-02
437636.47
437636.47
437636.47
Bill
Thoughtworks
2016-10-04
Bill
Babbleopia
2016-10-02
437636.47
437636.47
437636.47
Bill
Thoughtworks
2016-10-04
Bill
19
1
Chris
15
2
Maria
14
3
Bob
14
3
2016
3
Skidoo
Bill
523295.07
Skipstorm
Bill
Abata
2016-09-10
645098.45
NULL
161086.82
Abata
2016-10-14
161086.82
645098.45
350235.75
1
6337.15
287634.01
2
288796.14
539977.45
3
540070.04
748727.51
4
753670.77
998864.47
Bill
Babbleopia
2016-10-02
437636.47
437636.4700000000
437636.47
Bill
Thoughtworks
2016-10-04
Mita
146086.51
146086.51
151420.86
Abata
146086.51
function_name ([expression [, expression ... ]]) OVER ( window_definition )[ PARTITION BY expression [, ...] ]
[ ORDER BY expression [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...] ]
[ frame_clause ]{ RANGE | ROWS } frame_start
{ RANGE | ROWS } BETWEEN frame_start AND frame_endUNBOUNDED PRECEDING
value PRECEDING
CURRENT ROW
value FOLLOWING
UNBOUNDED FOLLOWINGCREATE TABLE opportunities (
id INT,
accountName VARCHAR(20),
name VARCHAR(128),
owner VARCHAR(7),
amount DECIMAL(10,2),
closeDate DATE,
stageName VARCHAR(11)
) ENGINE=columnstore;SELECT owner,
accountName,
CloseDate,
amount,
SUM(amount) OVER (ORDER BY CloseDate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) cumeWon,
MAX(amount) OVER (ORDER BY CloseDate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) runningMax
FROM opportunities
WHERE stageName='ClosedWon'
AND closeDate >= '2016-10-02' AND closeDate <= '2016-10-09'
ORDER BY CloseDate;SELECT owner,
accountName,
CloseDate,
amount,
SUM(amount) OVER (PARTITION BY owner ORDER BY CloseDate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) cumeWon,
MAX(amount) OVER (PARTITION BY owner ORDER BY CloseDate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) runningMax
FROM opportunities
WHERE stageName='ClosedWon'
AND closeDate >= '2016-10-02' AND closeDate <= '2016-10-09'
ORDER BY owner, CloseDate;SELECT owner,
wonCount,
rank() OVER (ORDER BY wonCount DESC) rank
FROM (
SELECT owner,
COUNT(*) wonCount
FROM opportunities
WHERE stageName='ClosedWon'
AND closeDate >= '2016-10-01' AND closeDate < '2016-12-31'
GROUP BY owner
) t
ORDER BY rank;SELECT a.YEAR,
a.quarter,
f.accountName firstAccountName,
f.owner firstOwner,
f.amount firstAmount,
l.accountName lastAccountName,
l.owner lastOwner,
l.amount lastAmount
FROM (
SELECT YEAR,
QUARTER,
MIN(firstId) firstId,
MIN(lastId) lastId
FROM (
SELECT YEAR(closeDate) YEAR,
quarter(closeDate) QUARTER,
first_value(id) OVER (PARTITION BY YEAR(closeDate), quarter(closeDate) ORDER BY closeDate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) firstId,
last_value(id) OVER (PARTITION BY YEAR(closeDate), quarter(closeDate) ORDER BY closeDate ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) lastId
FROM opportunities WHERE stageName='ClosedWon'
) t
GROUP BY YEAR, QUARTER ORDER BY YEAR,QUARTER
) a
JOIN opportunities f ON a.firstId = f.id
JOIN opportunities l ON a.lastId = l.id
ORDER BY YEAR, QUARTER;SELECT accountName,
closeDate,
amount currentOppAmount,
lag(amount) OVER (PARTITION BY accountName ORDER BY closeDate) priorAmount, lead(amount) OVER (PARTITION BY accountName ORDER BY closeDate) nextAmount
FROM opportunities
ORDER BY accountName, closeDate
LIMIT 9;SELECT t.quartile,
MIN(t.amount) MIN,
MAX(t.amount) MAX
FROM (
SELECT amount,
ntile(4) OVER (ORDER BY amount ASC) quartile
FROM opportunities
WHERE closeDate >= '2016-10-01' AND closeDate <= '2016-12-31'
) t
GROUP BY quartile
ORDER BY quartile;SELECT owner,
accountName,
CloseDate,
amount,
percentile_cont(0.5) within GROUP (ORDER BY amount) OVER (PARTITION BY owner) pct_cont,
percentile_disc(0.5) within GROUP (ORDER BY amount) OVER (PARTITION BY owner) pct_disc
FROM opportunities
WHERE stageName='ClosedWon'
AND closeDate >= '2016-10-02' AND closeDate <= '2016-10-09'
ORDER BY owner, CloseDate;