Window 函式
適用於：
Databricks SQL Databricks Runtime
在一組數據列上運作的函式，稱為「window」，並根據數據列群組計算每個數據列的傳回值。 Window 函式可用於處理工作，例如計算移動平均、計算累計統計數據，或存取指定目前數據列相對位置的數據列值。
語法
function OVER { window_name | ( window_name ) | window_spec }
function
{ ranking_function | analytic_function | aggregate_function }
over_clause
OVER { window_name | ( window_name ) | window_spec }
window_spec
( [ PARTITION BY partition [ , ... ] ] [ order_by ] [ window_frame ] )
Parameters
函數
在 window上運作的函式。 函式的不同類別支援 window 規格的不同組態。
ranking_function
任何 排名 window 函式都會。
如果指定window_spec必須包含
子句，但不能包含 window_frame 子句。 analytic_function
任何 分析 window 函式。
aggregate_function
如果指定，函式不得包含 FILTER 子句。
window_name
window_spec
這個子句會定義如何將數據列分組、在群組內排序，以及函式在 partition 內作用於哪些數據列。
partition
一或多個表達式，用來指定定義函式運作範圍的數據列群組。 如果未指定任何 PARTITION 子句，則 partition 包含所有行。
order_by
ORDER BY 子句 指定 partition中列的順序。
window_frame
window frame 子句 指定為在 partition 中匯總或分析函式運行的數據列的滑動子集。
您可以將 SORT BY 指定為 ORDER BY的別名。
您也可以將 DISTRIBUTE BY 指定為 PARTITION BY 的別名。 在沒有 ORDER BY的情況下，您可以使用 CLUSTER BY 作為 PARTITION BY 的別名。
範例
> CREATE TABLE employees
(name STRING, dept STRING, salary INT, age INT);
> INSERT INTO employees
VALUES ('Lisa', 'Sales', 10000, 35),
('Evan', 'Sales', 32000, 38),
('Fred', 'Engineering', 21000, 28),
('Alex', 'Sales', 30000, 33),
('Tom', 'Engineering', 23000, 33),
('Jane', 'Marketing', 29000, 28),
('Jeff', 'Marketing', 35000, 38),
('Paul', 'Engineering', 29000, 23),
('Chloe', 'Engineering', 23000, 25);
> SELECT name, dept, salary, age FROM employees;
Chloe Engineering 23000 25
Fred Engineering 21000 28
Paul Engineering 29000 23
Helen Marketing 29000 40
Tom Engineering 23000 33
Jane Marketing 29000 28
Jeff Marketing 35000 38
Evan Sales 32000 38
Lisa Sales 10000 35
Alex Sales 30000 33
> SELECT name,
dept,
RANK() OVER (PARTITION BY dept ORDER BY salary) AS rank
FROM employees;
Lisa Sales 10000 1
Alex Sales 30000 2
Evan Sales 32000 3
Fred Engineering 21000 1
Tom Engineering 23000 2
Chloe Engineering 23000 2
Paul Engineering 29000 4
Helen Marketing 29000 1
Jane Marketing 29000 1
Jeff Marketing 35000 3
> SELECT name,
dept,
DENSE_RANK() OVER (PARTITION BY dept ORDER BY salary
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS dense_rank
FROM employees;
Lisa Sales 10000 1
Alex Sales 30000 2
Evan Sales 32000 3
Fred Engineering 21000 1
Tom Engineering 23000 2
Chloe Engineering 23000 2
Paul Engineering 29000 3
Helen Marketing 29000 1
Jane Marketing 29000 1
Jeff Marketing 35000 2
> SELECT name,
dept,
age,
CUME_DIST() OVER (PARTITION BY dept ORDER BY age
RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cume_dist
FROM employees;
Alex Sales 33 0.3333333333333333
Lisa Sales 35 0.6666666666666666
Evan Sales 38 1.0
Paul Engineering 23 0.25
Chloe Engineering 25 0.50
Fred Engineering 28 0.75
Tom Engineering 33 1.0
Jane Marketing 28 0.3333333333333333
Jeff Marketing 38 0.6666666666666666
Helen Marketing 40 1.0
> SELECT name,
dept,
salary,
MIN(salary) OVER (PARTITION BY dept ORDER BY salary) AS min
FROM employees;
Lisa Sales 10000 10000
Alex Sales 30000 10000
Evan Sales 32000 10000
Helen Marketing 29000 29000
Jane Marketing 29000 29000
Jeff Marketing 35000 29000
Fred Engineering 21000 21000
Tom Engineering 23000 21000
Chloe Engineering 23000 21000
Paul Engineering 29000 21000
> SELECT name,
salary,
LAG(salary) OVER (PARTITION BY dept ORDER BY salary) AS lag,
LEAD(salary, 1, 0) OVER (PARTITION BY dept ORDER BY salary) AS lead
FROM employees;
Lisa Sales 10000 NULL 30000
Alex Sales 30000 10000 32000
Evan Sales 32000 30000 0
Fred Engineering 21000 NULL 23000
Chloe Engineering 23000 21000 23000
Tom Engineering 23000 23000 29000
Paul Engineering 29000 23000 0
Helen Marketing 29000 NULL 29000
Jane Marketing 29000 29000 35000
Jeff Marketing 35000 29000 0