> ## Documentation Index > Fetch the complete documentation index at: https://private-7c7dfe99-mintlify-8c05c8a2.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. > 窗口函数概述页面 # 窗口函数 窗口函数可让你对与当前行相关的一组行进行计算。 它们可用于执行与聚合函数类似的计算，但不同之处在于，窗口函数不会将多行分组成单个输出，而是仍会返回各个行。
## 标准窗口函数
ClickHouse 支持窗口和窗口函数的标准 SQL 语法。 下表展示了当前支持的功能： | 功能 | 支持？ | 说明 | | ------------------------------------------------------------------- | --- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 临时窗口定义 (`count(*) OVER (PARTITION BY id ORDER BY time DESC)`) | ✅ | | | 包含窗口函数的表达式，例如 `(count(*) OVER ()) / 2` | ✅ | | | `WINDOW` 子句 (`SELECT ... FROM table WINDOW w AS (PARTITION BY id)`) | ✅ | | | `ROWS` 窗口帧 | ✅ | | | `RANGE` 窗口帧 | ✅ | 未显式指定窗口帧时，默认使用它 (`RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`) 。 | | `DateTime` `RANGE OFFSET` 窗口帧的 `INTERVAL` 语法 | ❌ | 请改为直接指定秒数 (`RANGE` 适用于任何数值类型) 。 | | `GROUPS` 窗口帧 | ❌ | | | 在窗口帧上计算聚合函数 (`sum(value) OVER (ORDER BY time)`) | ✅ | 支持所有聚合函数。 | | `rank()`, `dense_rank()`/`denseRank()`, `row_number()` | ✅ | | | `percent_rank()`/`percentRank()` | ✅ | 可高效计算某个值在分区内的相对排名。它可替代更冗长且计算开销更高的手动 SQL 写法：`ifNull((rank() OVER (PARTITION BY x ORDER BY y) - 1) / nullif(count(1) OVER (PARTITION BY x) - 1, 0), 0)`。 | | `cume_dist()` | ✅ | 计算某个值在一组值中的累计分布。返回值小于或等于当前行值的行所占的百分比。 | | `lag/lead(value, offset)` | ✅ | 你也可以使用以下任一变通方法：
1) `any(value) OVER (... ROWS BETWEEN PRECEDING AND PRECEDING)`，或者对于 `lead`，使用 `FOLLOWING` 替代 `PRECEDING`
2) `lagInFrame/leadInFrame`，它们与前者类似，但会遵循窗口帧。若要获得与 `lag/lead` 完全相同的行为，请使用 `ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`。 | | `ntile(buckets)` | ✅ | 例如，可将窗口指定为 `(PARTITION BY x ORDER BY y ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)`。 |
## 语法
```text theme={null} aggregate_function (column_name) OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column] [ROWS or RANGE expression_to_bound_rows_within_the_group]] | [window_name]) FROM table_name WINDOW window_name as ([ [PARTITION BY grouping_column] [ORDER BY sorting_column] [ROWS or RANGE expression_to_bound_rows_within_the_group] ]) ``` * `PARTITION BY` - 定义如何将结果集划分为多个组。 * `ORDER BY` - 定义在计算 aggregate\_function 时，如何对组内的行进行排序。 * `ROWS or RANGE` - 定义窗口帧的边界，aggregate\_function 在窗口帧内进行计算。 * `WINDOW` - 允许多个表达式使用同一个窗口定义。 ```text theme={null} PARTITION ┌─────────────────┐ <-- UNBOUNDED PRECEDING (BEGINNING of the PARTITION) │ │ │ │ │=================│ <-- N PRECEDING <─┐ │ N ROWS │ │ F │ Before CURRENT │ │ R │~~~~~~~~~~~~~~~~~│ <-- CURRENT ROW │ A │ M ROWS │ │ M │ After CURRENT │ │ E │=================│ <-- M FOLLOWING <─┘ │ │ │ │ └─────────────────┘ <--- UNBOUNDED FOLLOWING (END of the PARTITION) ```
## 仅可作为窗口函数使用的函数
以下函数只能作为窗口函数使用。其中大多数是标准 SQL 函数；`lagInFrame`、`leadInFrame` 和 `nonNegativeDerivative` 是 ClickHouse 扩展。 | Function | Description | | ---------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------- | | [`row_number()`](/zh/reference/functions/window-functions/row_number) | 从 1 开始对当前行在其分区内进行编号。 | | [`first_value(x)`](/zh/reference/functions/window-functions/first_value) | 返回其有序窗口帧中计算出的第一个值。 | | [`last_value(x)`](/zh/reference/functions/window-functions/last_value) | 返回其有序窗口帧中计算出的最后一个值。 | | [`nth_value(x, offset)`](/zh/reference/functions/window-functions/nth_value) | 返回其有序窗口帧中第 n 行 (偏移量) 对应计算结果里的第一个非 NULL 值。 | | [`rank()`](/zh/reference/functions/window-functions/rank) | 对当前行在其分区内进行排名，排名中会有空缺。 | | [`dense_rank()`](/zh/reference/functions/window-functions/dense_rank) | 对当前行在其分区内进行排名，排名中没有空缺。 | | [`lagInFrame(x)`](/zh/reference/functions/window-functions/lagInFrame) | 返回其有序窗口帧中位于当前行之前指定物理偏移位置的行所计算出的值。 | | [`leadInFrame(x)`](/zh/reference/functions/window-functions/leadInFrame) | 返回其有序窗口帧中位于当前行之后偏移若干行的位置所计算出的值。 | | [`nonNegativeDerivative(metric_column, timestamp_column[, INTERVAL X UNITS])`](/zh/reference/functions/window-functions/nonNegativeDerivative) | 计算 `metric_column` 相对于 `timestamp_column` 的非负导数。这是 ClickHouse 特有的函数。 |
## 示例
下面来看一些窗口函数的使用示例。
### 为行编号
```sql theme={null} CREATE TABLE salaries ( `team` String, `player` String, `salary` UInt32, `position` String ) Engine = Memory; INSERT INTO salaries FORMAT Values ('Port Elizabeth Barbarians', 'Gary Chen', 195000, 'F'), ('New Coreystad Archdukes', 'Charles Juarez', 190000, 'F'), ('Port Elizabeth Barbarians', 'Michael Stanley', 150000, 'D'), ('New Coreystad Archdukes', 'Scott Harrison', 150000, 'D'), ('Port Elizabeth Barbarians', 'Robert George', 195000, 'M'); ``` ```sql theme={null} SELECT player, salary, row_number() OVER (ORDER BY salary ASC) AS row FROM salaries; ``` ```text theme={null} ┌─player──────────┬─salary─┬─row─┐ │ Michael Stanley │ 150000 │ 1 │ │ Scott Harrison │ 150000 │ 2 │ │ Charles Juarez │ 190000 │ 3 │ │ Gary Chen │ 195000 │ 4 │ │ Robert George │ 195000 │ 5 │ └─────────────────┴────────┴─────┘ ``` ```sql theme={null} SELECT player, salary, row_number() OVER (ORDER BY salary ASC) AS row, rank() OVER (ORDER BY salary ASC) AS rank, dense_rank() OVER (ORDER BY salary ASC) AS denseRank FROM salaries; ``` ```text theme={null} ┌─player──────────┬─salary─┬─row─┬─rank─┬─denseRank─┐ │ Michael Stanley │ 150000 │ 1 │ 1 │ 1 │ │ Scott Harrison │ 150000 │ 2 │ 1 │ 1 │ │ Charles Juarez │ 190000 │ 3 │ 3 │ 2 │ │ Gary Chen │ 195000 │ 4 │ 4 │ 3 │ │ Robert George │ 195000 │ 5 │ 4 │ 3 │ └─────────────────┴────────┴─────┴──────┴───────────┘ ```
### 聚合函数
将每位球员的薪资与其所在球队的平均薪资进行比较。 ```sql theme={null} SELECT player, salary, team, avg(salary) OVER (PARTITION BY team) AS teamAvg, salary - teamAvg AS diff FROM salaries; ``` ```text theme={null} ┌─player──────────┬─salary─┬─team──────────────────────┬─teamAvg─┬───diff─┐ │ Charles Juarez │ 190000 │ New Coreystad Archdukes │ 170000 │ 20000 │ │ Scott Harrison │ 150000 │ New Coreystad Archdukes │ 170000 │ -20000 │ │ Gary Chen │ 195000 │ Port Elizabeth Barbarians │ 180000 │ 15000 │ │ Michael Stanley │ 150000 │ Port Elizabeth Barbarians │ 180000 │ -30000 │ │ Robert George │ 195000 │ Port Elizabeth Barbarians │ 180000 │ 15000 │ └─────────────────┴────────┴───────────────────────────┴─────────┴────────┘ ``` 比较每位球员的薪资与其所在球队的最高薪资。 ```sql theme={null} SELECT player, salary, team, max(salary) OVER (PARTITION BY team) AS teamMax, salary - teamMax AS diff FROM salaries; ``` ```text theme={null} ┌─player──────────┬─salary─┬─team──────────────────────┬─teamMax─┬───diff─┐ │ Charles Juarez │ 190000 │ New Coreystad Archdukes │ 190000 │ 0 │ │ Scott Harrison │ 150000 │ New Coreystad Archdukes │ 190000 │ -40000 │ │ Gary Chen │ 195000 │ Port Elizabeth Barbarians │ 195000 │ 0 │ │ Michael Stanley │ 150000 │ Port Elizabeth Barbarians │ 195000 │ -45000 │ │ Robert George │ 195000 │ Port Elizabeth Barbarians │ 195000 │ 0 │ └─────────────────┴────────┴───────────────────────────┴─────────┴────────┘ ```
### 按列分区
```sql theme={null} CREATE TABLE wf_partition ( `part_key` UInt64, `value` UInt64, `order` UInt64 ) ENGINE = Memory; INSERT INTO wf_partition FORMAT Values (1,1,1), (1,2,2), (1,3,3), (2,0,0), (3,0,0); SELECT part_key, value, order, groupArray(value) OVER (PARTITION BY part_key) AS frame_values FROM wf_partition ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1,2,3] │ <┐ │ 1 │ 2 │ 2 │ [1,2,3] │ │ 1-st group │ 1 │ 3 │ 3 │ [1,2,3] │ <┘ │ 2 │ 0 │ 0 │ [0] │ <- 2-nd group │ 3 │ 0 │ 0 │ [0] │ <- 3-d group └──────────┴───────┴───────┴──────────────┘ ```
### 窗口帧界定
```sql theme={null} CREATE TABLE wf_frame ( `part_key` UInt64, `value` UInt64, `order` UInt64 ) ENGINE = Memory; INSERT INTO wf_frame FORMAT Values (1,1,1), (1,2,2), (1,3,3), (1,4,4), (1,5,5); ``` ```sql theme={null} -- Frame is bounded by bounds of a partition (BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) SELECT part_key, value, order, groupArray(value) OVER ( PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1,2,3,4,5] │ │ 1 │ 2 │ 2 │ [1,2,3,4,5] │ │ 1 │ 3 │ 3 │ [1,2,3,4,5] │ │ 1 │ 4 │ 4 │ [1,2,3,4,5] │ │ 1 │ 5 │ 5 │ [1,2,3,4,5] │ └──────────┴───────┴───────┴──────────────┘ ``` ```sql theme={null} -- short form - no bound expression, no order by, -- an equalent of `ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING` SELECT part_key, value, order, groupArray(value) OVER (PARTITION BY part_key) AS frame_values_short, groupArray(value) OVER (PARTITION BY part_key ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values_short─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1,2,3,4,5] │ [1,2,3,4,5] │ │ 1 │ 2 │ 2 │ [1,2,3,4,5] │ [1,2,3,4,5] │ │ 1 │ 3 │ 3 │ [1,2,3,4,5] │ [1,2,3,4,5] │ │ 1 │ 4 │ 4 │ [1,2,3,4,5] │ [1,2,3,4,5] │ │ 1 │ 5 │ 5 │ [1,2,3,4,5] │ [1,2,3,4,5] │ └──────────┴───────┴───────┴────────────────────┴──────────────┘ ``` ```sql theme={null} -- frame is bounded by the beginning of a partition and the current row SELECT part_key, value, order, groupArray(value) OVER ( PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1] │ │ 1 │ 2 │ 2 │ [1,2] │ │ 1 │ 3 │ 3 │ [1,2,3] │ │ 1 │ 4 │ 4 │ [1,2,3,4] │ │ 1 │ 5 │ 5 │ [1,2,3,4,5] │ └──────────┴───────┴───────┴──────────────┘ ``` ```sql theme={null} -- short form (frame is bounded by the beginning of a partition and the current row) -- an equalent of `ORDER BY order ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` SELECT part_key, value, order, groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC) AS frame_values_short, groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values_short─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1] │ [1] │ │ 1 │ 2 │ 2 │ [1,2] │ [1,2] │ │ 1 │ 3 │ 3 │ [1,2,3] │ [1,2,3] │ │ 1 │ 4 │ 4 │ [1,2,3,4] │ [1,2,3,4] │ │ 1 │ 5 │ 5 │ [1,2,3,4,5] │ [1,2,3,4,5] │ └──────────┴───────┴───────┴────────────────────┴──────────────┘ ``` ```sql theme={null} -- frame is bounded by the beginning of a partition and the current row, but order is backward SELECT part_key, value, order, groupArray(value) OVER (PARTITION BY part_key ORDER BY order DESC) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [5,4,3,2,1] │ │ 1 │ 2 │ 2 │ [5,4,3,2] │ │ 1 │ 3 │ 3 │ [5,4,3] │ │ 1 │ 4 │ 4 │ [5,4] │ │ 1 │ 5 │ 5 │ [5] │ └──────────┴───────┴───────┴──────────────┘ ``` ```sql theme={null} -- sliding frame - 1 PRECEDING ROW AND CURRENT ROW SELECT part_key, value, order, groupArray(value) OVER ( PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1] │ │ 1 │ 2 │ 2 │ [1,2] │ │ 1 │ 3 │ 3 │ [2,3] │ │ 1 │ 4 │ 4 │ [3,4] │ │ 1 │ 5 │ 5 │ [4,5] │ └──────────┴───────┴───────┴──────────────┘ ``` ```sql theme={null} -- sliding frame - ROWS BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING SELECT part_key, value, order, groupArray(value) OVER ( PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING ) AS frame_values FROM wf_frame ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┐ │ 1 │ 1 │ 1 │ [1,2,3,4,5] │ │ 1 │ 2 │ 2 │ [1,2,3,4,5] │ │ 1 │ 3 │ 3 │ [2,3,4,5] │ │ 1 │ 4 │ 4 │ [3,4,5] │ │ 1 │ 5 │ 5 │ [4,5] │ └──────────┴───────┴───────┴──────────────┘ ``` ```sql theme={null} -- row_number does not respect the frame, so rn_1 = rn_2 = rn_3 != rn_4 SELECT part_key, value, order, groupArray(value) OVER w1 AS frame_values, row_number() OVER w1 AS rn_1, sum(1) OVER w1 AS rn_2, row_number() OVER w2 AS rn_3, sum(1) OVER w2 AS rn_4 FROM wf_frame WINDOW w1 AS (PARTITION BY part_key ORDER BY order DESC), w2 AS ( PARTITION BY part_key ORDER BY order DESC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW ) ORDER BY part_key ASC, value ASC; ┌─part_key─┬─value─┬─order─┬─frame_values─┬─rn_1─┬─rn_2─┬─rn_3─┬─rn_4─┐ │ 1 │ 1 │ 1 │ [5,4,3,2,1] │ 5 │ 5 │ 5 │ 2 │ │ 1 │ 2 │ 2 │ [5,4,3,2] │ 4 │ 4 │ 4 │ 2 │ │ 1 │ 3 │ 3 │ [5,4,3] │ 3 │ 3 │ 3 │ 2 │ │ 1 │ 4 │ 4 │ [5,4] │ 2 │ 2 │ 2 │ 2 │ │ 1 │ 5 │ 5 │ [5] │ 1 │ 1 │ 1 │ 1 │ └──────────┴───────┴───────┴──────────────┴──────┴──────┴──────┴──────┘ ``` ```sql theme={null} -- first_value and last_value respect the frame SELECT groupArray(value) OVER w1 AS frame_values_1, first_value(value) OVER w1 AS first_value_1, last_value(value) OVER w1 AS last_value_1, groupArray(value) OVER w2 AS frame_values_2, first_value(value) OVER w2 AS first_value_2, last_value(value) OVER w2 AS last_value_2 FROM wf_frame WINDOW w1 AS (PARTITION BY part_key ORDER BY order ASC), w2 AS (PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) ORDER BY part_key ASC, value ASC; ┌─frame_values_1─┬─first_value_1─┬─last_value_1─┬─frame_values_2─┬─first_value_2─┬─last_value_2─┐ │ [1] │ 1 │ 1 │ [1] │ 1 │ 1 │ │ [1,2] │ 1 │ 2 │ [1,2] │ 1 │ 2 │ │ [1,2,3] │ 1 │ 3 │ [2,3] │ 2 │ 3 │ │ [1,2,3,4] │ 1 │ 4 │ [3,4] │ 3 │ 4 │ │ [1,2,3,4,5] │ 1 │ 5 │ [4,5] │ 4 │ 5 │ └────────────────┴───────────────┴──────────────┴────────────────┴───────────────┴──────────────┘ ``` ```sql theme={null} -- second value within the frame SELECT groupArray(value) OVER w1 AS frame_values_1, nth_value(value, 2) OVER w1 AS second_value FROM wf_frame WINDOW w1 AS (PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) ORDER BY part_key ASC, value ASC; ┌─frame_values_1─┬─second_value─┐ │ [1] │ 0 │ │ [1,2] │ 2 │ │ [1,2,3] │ 2 │ │ [1,2,3,4] │ 2 │ │ [2,3,4,5] │ 3 │ └────────────────┴──────────────┘ ``` ```sql theme={null} -- second value within the frame + Null for missing values SELECT groupArray(value) OVER w1 AS frame_values_1, nth_value(toNullable(value), 2) OVER w1 AS second_value FROM wf_frame WINDOW w1 AS (PARTITION BY part_key ORDER BY order ASC ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) ORDER BY part_key ASC, value ASC; ┌─frame_values_1─┬─second_value─┐ │ [1] │ ᴺᵁᴸᴸ │ │ [1,2] │ 2 │ │ [1,2,3] │ 2 │ │ [1,2,3,4] │ 2 │ │ [2,3,4,5] │ 3 │ └────────────────┴──────────────┘ ```
## 真实场景示例
以下示例用于解决常见的实际问题。
### 各部门的最高/总工资
```sql theme={null} CREATE TABLE employees ( `department` String, `employee_name` String, `salary` Float ) ENGINE = Memory; INSERT INTO employees FORMAT Values ('Finance', 'Jonh', 200), ('Finance', 'Joan', 210), ('Finance', 'Jean', 505), ('IT', 'Tim', 200), ('IT', 'Anna', 300), ('IT', 'Elen', 500); ``` ```sql theme={null} SELECT department, employee_name AS emp, salary, max_salary_per_dep, total_salary_per_dep, round((salary / total_salary_per_dep) * 100, 2) AS `share_per_dep(%)` FROM ( SELECT department, employee_name, salary, max(salary) OVER wndw AS max_salary_per_dep, sum(salary) OVER wndw AS total_salary_per_dep FROM employees WINDOW wndw AS ( PARTITION BY department ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING ) ORDER BY department ASC, employee_name ASC ); ┌─department─┬─emp──┬─salary─┬─max_salary_per_dep─┬─total_salary_per_dep─┬─share_per_dep(%)─┐ │ Finance │ Jean │ 505 │ 505 │ 915 │ 55.19 │ │ Finance │ Joan │ 210 │ 505 │ 915 │ 22.95 │ │ Finance │ Jonh │ 200 │ 505 │ 915 │ 21.86 │ │ IT │ Anna │ 300 │ 500 │ 1000 │ 30 │ │ IT │ Elen │ 500 │ 500 │ 1000 │ 50 │ │ IT │ Tim │ 200 │ 500 │ 1000 │ 20 │ └────────────┴──────┴────────┴────────────────────┴──────────────────────┴──────────────────┘ ```
### 累积和
```sql theme={null} CREATE TABLE warehouse ( `item` String, `ts` DateTime, `value` Float ) ENGINE = Memory INSERT INTO warehouse VALUES ('sku38', '2020-01-01', 9), ('sku38', '2020-02-01', 1), ('sku38', '2020-03-01', -4), ('sku1', '2020-01-01', 1), ('sku1', '2020-02-01', 1), ('sku1', '2020-03-01', 1); ``` ```sql theme={null} SELECT item, ts, value, sum(value) OVER (PARTITION BY item ORDER BY ts ASC) AS stock_balance FROM warehouse ORDER BY item ASC, ts ASC; ┌─item──┬──────────────────ts─┬─value─┬─stock_balance─┐ │ sku1 │ 2020-01-01 00:00:00 │ 1 │ 1 │ │ sku1 │ 2020-02-01 00:00:00 │ 1 │ 2 │ │ sku1 │ 2020-03-01 00:00:00 │ 1 │ 3 │ │ sku38 │ 2020-01-01 00:00:00 │ 9 │ 9 │ │ sku38 │ 2020-02-01 00:00:00 │ 1 │ 10 │ │ sku38 │ 2020-03-01 00:00:00 │ -4 │ 6 │ └───────┴─────────────────────┴───────┴───────────────┘ ```
### 移动平均 / 滑动平均 (每 3 行)
```sql theme={null} CREATE TABLE sensors ( `metric` String, `ts` DateTime, `value` Float ) ENGINE = Memory; insert into sensors values('cpu_temp', '2020-01-01 00:00:00', 87), ('cpu_temp', '2020-01-01 00:00:01', 77), ('cpu_temp', '2020-01-01 00:00:02', 93), ('cpu_temp', '2020-01-01 00:00:03', 87), ('cpu_temp', '2020-01-01 00:00:04', 87), ('cpu_temp', '2020-01-01 00:00:05', 87), ('cpu_temp', '2020-01-01 00:00:06', 87), ('cpu_temp', '2020-01-01 00:00:07', 87); ``` ```sql theme={null} SELECT metric, ts, value, avg(value) OVER ( PARTITION BY metric ORDER BY ts ASC ROWS BETWEEN 2 PRECEDING AND CURRENT ROW ) AS moving_avg_temp FROM sensors ORDER BY metric ASC, ts ASC; ┌─metric───┬──────────────────ts─┬─value─┬───moving_avg_temp─┐ │ cpu_temp │ 2020-01-01 00:00:00 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:00:01 │ 77 │ 82 │ │ cpu_temp │ 2020-01-01 00:00:02 │ 93 │ 85.66666666666667 │ │ cpu_temp │ 2020-01-01 00:00:03 │ 87 │ 85.66666666666667 │ │ cpu_temp │ 2020-01-01 00:00:04 │ 87 │ 89 │ │ cpu_temp │ 2020-01-01 00:00:05 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:00:06 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:00:07 │ 87 │ 87 │ └──────────┴─────────────────────┴───────┴───────────────────┘ ```
### 移动平均 / 滑动平均 (每 10 秒)
```sql theme={null} SELECT metric, ts, value, avg(value) OVER (PARTITION BY metric ORDER BY ts RANGE BETWEEN 10 PRECEDING AND CURRENT ROW) AS moving_avg_10_seconds_temp FROM sensors ORDER BY metric ASC, ts ASC; ┌─metric───┬──────────────────ts─┬─value─┬─moving_avg_10_seconds_temp─┐ │ cpu_temp │ 2020-01-01 00:00:00 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:01:10 │ 77 │ 77 │ │ cpu_temp │ 2020-01-01 00:02:20 │ 93 │ 93 │ │ cpu_temp │ 2020-01-01 00:03:30 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:04:40 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:05:50 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:06:00 │ 87 │ 87 │ │ cpu_temp │ 2020-01-01 00:07:10 │ 87 │ 87 │ └──────────┴─────────────────────┴───────┴────────────────────────────┘ ```
### 移动平均 / 滑动平均 (每 10 天)
温度以秒级精度存储，但使用 `Range` 和 `ORDER BY toDate(ts)` 时，我们构造出的窗口帧大小为 10 个单位；而由于使用了 `toDate(ts)`，这里的单位是天。 ```sql theme={null} CREATE TABLE sensors ( `metric` String, `ts` DateTime, `value` Float ) ENGINE = Memory; insert into sensors values('ambient_temp', '2020-01-01 00:00:00', 16), ('ambient_temp', '2020-01-01 12:00:00', 16), ('ambient_temp', '2020-01-02 11:00:00', 9), ('ambient_temp', '2020-01-02 12:00:00', 9), ('ambient_temp', '2020-02-01 10:00:00', 10), ('ambient_temp', '2020-02-01 12:00:00', 10), ('ambient_temp', '2020-02-10 12:00:00', 12), ('ambient_temp', '2020-02-10 13:00:00', 12), ('ambient_temp', '2020-02-20 12:00:01', 16), ('ambient_temp', '2020-03-01 12:00:00', 16), ('ambient_temp', '2020-03-01 12:00:00', 16), ('ambient_temp', '2020-03-01 12:00:00', 16); ``` ```sql theme={null} SELECT metric, ts, value, round(avg(value) OVER (PARTITION BY metric ORDER BY toDate(ts) RANGE BETWEEN 10 PRECEDING AND CURRENT ROW),2) AS moving_avg_10_days_temp FROM sensors ORDER BY metric ASC, ts ASC; ┌─metric───────┬──────────────────ts─┬─value─┬─moving_avg_10_days_temp─┐ │ ambient_temp │ 2020-01-01 00:00:00 │ 16 │ 16 │ │ ambient_temp │ 2020-01-01 12:00:00 │ 16 │ 16 │ │ ambient_temp │ 2020-01-02 11:00:00 │ 9 │ 12.5 │ │ ambient_temp │ 2020-01-02 12:00:00 │ 9 │ 12.5 │ │ ambient_temp │ 2020-02-01 10:00:00 │ 10 │ 10 │ │ ambient_temp │ 2020-02-01 12:00:00 │ 10 │ 10 │ │ ambient_temp │ 2020-02-10 12:00:00 │ 12 │ 11 │ │ ambient_temp │ 2020-02-10 13:00:00 │ 12 │ 11 │ │ ambient_temp │ 2020-02-20 12:00:01 │ 16 │ 13.33 │ │ ambient_temp │ 2020-03-01 12:00:00 │ 16 │ 16 │ │ ambient_temp │ 2020-03-01 12:00:00 │ 16 │ 16 │ │ ambient_temp │ 2020-03-01 12:00:00 │ 16 │ 16 │ └──────────────┴─────────────────────┴───────┴─────────────────────────┘ ```
## 参考资料
### GitHub Issues
有关窗口函数初步支持的路线图，请参见[此 issue](https://github.com/ClickHouse/ClickHouse/issues/18097)。 所有与窗口函数相关的 GitHub issue 都带有 [comp-window-functions](https://github.com/ClickHouse/ClickHouse/labels/comp-window-functions) 标签。
### 测试
这些测试包含当前已支持语法的示例： [https://github.com/ClickHouse/ClickHouse/blob/master/tests/performance/window\_functions.xml](https://github.com/ClickHouse/ClickHouse/blob/master/tests/performance/window\_functions.xml) [https://github.com/ClickHouse/ClickHouse/blob/master/tests/queries/0\_stateless/01591\_window\_functions.sql](https://github.com/ClickHouse/ClickHouse/blob/master/tests/queries/0\_stateless/01591\_window\_functions.sql)
### Postgres 文档
[https://www.postgresql.org/docs/current/sql-select.html#SQL-WINDOW](https://www.postgresql.org/docs/current/sql-select.html#SQL-WINDOW) [https://www.postgresql.org/docs/devel/sql-expressions.html#SYNTAX-WINDOW-FUNCTIONS](https://www.postgresql.org/docs/devel/sql-expressions.html#SYNTAX-WINDOW-FUNCTIONS) [https://www.postgresql.org/docs/devel/functions-window.html](https://www.postgresql.org/docs/devel/functions-window.html) [https://www.postgresql.org/docs/devel/tutorial-window.html](https://www.postgresql.org/docs/devel/tutorial-window.html)
### MySQL 文档
[https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html](https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html) [https://dev.mysql.com/doc/refman/8.0/en/window-functions-usage.html](https://dev.mysql.com/doc/refman/8.0/en/window-functions-usage.html) [https://dev.mysql.com/doc/refman/8.0/en/window-functions-frames.html](https://dev.mysql.com/doc/refman/8.0/en/window-functions-frames.html)
## 相关内容
* 博客：[在 ClickHouse 中处理时序数据](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse) * 博客：[适用于 Git 提交序列的窗口函数和数组函数](https://clickhouse.com/blog/clickhouse-window-array-functions-git-commits) * 博客：[将数据导入 ClickHouse - 第 3 部分 - 使用 S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3)