Spark SQL 的窗口函数

提示

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窗口函数对一组行（称为窗口）进行操作，并基于该组行计算每行的返回值。窗口函数对于处理诸如计算移动平均值、计算累积统计数据或访问给定当前行相对位置的行值等任务非常有用。

语法

Spark SQL 的窗口函数结构为：

window_function OVER
( [  { PARTITION | DISTRIBUTE } BY partition_col_name = partition_col_val ( [ , ... ] ) ]
  { ORDER | SORT } BY expression [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [ , ... ]
  [ window_frame ] )

参数：

window_function（窗口函数）
- Ranking Functions：排序函数 RANK | DENSE_RANK | PERCENT_RANK | NTILE | ROW_NUMBER
- Analytic Functions：解析函数 CUME_DIST | LAG | LEAD
- Aggregate Functions：聚合函数 MAX | MIN | COUNT | SUM | AVG | ...

有关 Spark 聚合函数的完整列表，请参阅内置聚合函数文档。

window_frame（窗框）：指定窗口的起始行和结束位置
- 语法：{ RANGE | ROWS } { frame_start | BETWEEN frame_start AND frame_end }
- frame_start 和 frame_end 具有以下语法：UNBOUNDED PRECEDING | offset PRECEDING | CURRENT ROW | offset FOLLOWING | UNBOUNDED FOLLOWING
- 其中 offset 是指定相对于当前行位置的偏移。如果省略 frame_end，则默认为当前行。

示例

以下是一些示例讲解：

CREATE TABLE employees (name STRING, dept STRING, salary INT, age INT);

INSERT INTO employees VALUES ("Lisa", "Sales", 10000, 35);
INSERT INTO employees VALUES ("Evan", "Sales", 32000, 38);
INSERT INTO employees VALUES ("Fred", "Engineering", 21000, 28);
INSERT INTO employees VALUES ("Alex", "Sales", 30000, 33);
INSERT INTO employees VALUES ("Tom", "Engineering", 23000, 33);
INSERT INTO employees VALUES ("Jane", "Marketing", 29000, 28);
INSERT INTO employees VALUES ("Jeff", "Marketing", 35000, 38);
INSERT INTO employees VALUES ("Paul", "Engineering", 29000, 23);
INSERT INTO employees VALUES ("Chloe", "Engineering", 23000, 25);

SELECT * FROM employees;
+-----+-----------+------+-----+
| name|       dept|salary|  age|
+-----+-----------+------+-----+
|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|
+-----+-----------+------+-----+

-- 在对应部门的排序号
SELECT name, dept,
RANK() OVER (PARTITION BY dept ORDER BY salary) AS rank
FROM employees;
+-----+-----------+------+----+
| name|       dept|salary|rank|
+-----+-----------+------+----+
| 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;
+-----+-----------+------+----------+
| name|       dept|salary|dense_rank|
+-----+-----------+------+----------+
| 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;
+-----+-----------+------+------------------+
| name|       dept|age   |         cume_dist|
+-----+-----------+------+------------------+
| Alex|      Sales|    33|0.3333333333333333|
| Lisa|      Sales|    35|0.6666666666666666|
| Evan|      Sales|    38|               1.0|
| Paul|Engineering|    23|              0.25|
|Chloe|Engineering|    25|              0.75|
| Fred|Engineering|    28|              0.25|
|  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;
+-----+-----------+------+-----+
| name|       dept|salary|  min|
+-----+-----------+------+-----+
| 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;
+-----+-----------+------+-----+-----+
| name|       dept|salary|  lag| lead|
+-----+-----------+------+-----+-----+
| 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|
+-----+-----------+------+-----+-----+

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更新时间：2021-08-20 22:22:54 标签：sql spark 窗口函数