PRECEDING)`، أو `FOLLOWING` بدلًا من `PRECEDING` في حالة `lead`
2) `lagInFrame/leadInFrame`، وهما متشابهان لكنهما يراعيان إطار النافذة. للحصول على سلوك مطابق تمامًا لـ `lag/lead`، استخدم `ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING`. |
| `ntile(buckets)` | ✅ | حدِّد النافذة، على سبيل المثال، على النحو التالي: `(PARTITION BY x ORDER BY y ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)`. |
## الصيغة
```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` - يحدّد كيفية تقسيم مجموعة النتائج إلى مجموعات.
* `ORDER BY` - يحدّد كيفية ترتيب الصفوف داخل المجموعة أثناء حساب aggregate\_function.
* `ROWS or RANGE` - يحدّد حدود الإطار، ويُحسَب aggregate\_function ضمن هذا الإطار.
* `WINDOW` - يتيح لعدة تعبيرات استخدام تعريف النافذة نفسه.
```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)
```
## الدوال التي لا يمكن استخدامها إلا كدوال نافذة
لا يمكن استخدام الدوال التالية إلا كدوال نافذة. معظمها دوال SQL قياسية، بينما تُعد `lagInFrame` و`leadInFrame` و`nonNegativeDerivative` امتدادات خاصة بـ ClickHouse.
| Function | Description |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| [`row_number()`](/ar/reference/functions/window-functions/row_number) | رقِّم الصف الحالي داخل القسم الخاص به بدءًا من 1. |
| [`first_value(x)`](/ar/reference/functions/window-functions/first_value) | أرجِع أول قيمة جرى تقييمها ضمن الإطار المرتَّب. |
| [`last_value(x)`](/ar/reference/functions/window-functions/last_value) | أرجِع آخر قيمة جرى تقييمها ضمن الإطار المرتَّب. |
| [`nth_value(x, offset)`](/ar/reference/functions/window-functions/nth_value) | أرجِع أول قيمة non-NULL جرى تقييمها عند الصف رقم n (offset) ضمن الإطار المرتَّب. |
| [`rank()`](/ar/reference/functions/window-functions/rank) | امنح الصف الحالي ترتيبًا داخل القسم الخاص به مع وجود فجوات. |
| [`dense_rank()`](/ar/reference/functions/window-functions/dense_rank) | امنح الصف الحالي ترتيبًا داخل القسم الخاص به من دون فجوات. |
| [`lagInFrame(x)`](/ar/reference/functions/window-functions/lagInFrame) | أرجِع قيمة جرى تقييمها عند صف يسبق الصف الحالي بإزاحة مادية محددة ضمن الإطار المرتَّب. |
| [`leadInFrame(x)`](/ar/reference/functions/window-functions/leadInFrame) | أرجِع قيمة جرى تقييمها عند صف يلي الصف الحالي بعدد صفوف الإزاحة ضمن الإطار المرتَّب. |
| [`nonNegativeDerivative(metric_column, timestamp_column[, INTERVAL X UNITS])`](/ar/reference/functions/window-functions/nonNegativeDerivative) | احسب المشتقة غير السالبة لـ `metric_column` بالنسبة إلى `timestamp_column`. هذه الدالة خاصة بـ ClickHouse. |
## أمثلة
لنلقِ نظرة على بعض الأمثلة لكيفية استخدام دوال النافذة.
### ترقيم الصفوف
```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 │
└─────────────────┴────────┴─────┴──────┴───────────┘
```
### دوال التجميع
قارن راتب كل لاعب بمتوسط راتب فريقه.
```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 │
└─────────────────┴────────┴───────────────────────────┴─────────┴────────┘
```
قارن راتب كل لاعب بأقصى راتب في فريقه.
```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 │
└─────────────────┴────────┴───────────────────────────┴─────────┴────────┘
```
### التقسيم حسب العمود
```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
└──────────┴───────┴───────┴──────────────┘
```
### تعيين حدود الإطار
```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 │
└────────────────┴──────────────┘
```
## أمثلة واقعية
تتناول الأمثلة التالية مشكلات واقعية شائعة.
### الحد الأقصى/إجمالي الرواتب لكل قسم
```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 │
└────────────┴──────┴────────┴────────────────────┴──────────────────────┴──────────────────┘
```
### المجموع التراكمي
```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 │
└───────┴─────────────────────┴───────┴───────────────┘
```
### المتوسط المتحرك / المتوسط الانزلاقي (لكل 3 صفوف)
```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 │
└──────────┴─────────────────────┴───────┴───────────────────┘
```
### المتوسط المتحرك / المتوسط الانزلاقي (كل 10 ثوانٍ)
```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 │
└──────────┴─────────────────────┴───────┴────────────────────────────┘
```
### المتوسط المتحرك / المتوسط الانزلاقي (لكل 10 أيام)
تُخزَّن درجة الحرارة بدقة الثانية، ولكن باستخدام `Range` و `ORDER BY toDate(ts)` نُكوِّن إطارًا بحجم 10 وحدات، وبما أن `toDate(ts)` مستخدَمة هنا، فالوحدة هي اليوم.
```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 │
└──────────────┴─────────────────────┴───────┴─────────────────────────┘
```
## المراجع
### مشكلات GitHub
خطة الطريق للدعم الأولي لدوال النوافذ موجودة [في هذه المشكلة](https://github.com/ClickHouse/ClickHouse/issues/18097).
جميع مشكلات GitHub المتعلقة بدوال النوافذ تحمل الوسم [comp-window-functions](https://github.com/ClickHouse/ClickHouse/labels/comp-window-functions).
### الاختبارات
تتضمن هذه الاختبارات أمثلة على البنية النحوية المدعومة حاليًا:
[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)
### وثائق Postgres
[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)
### وثائق MySQL
[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)
## محتوى ذو صلة
* المدونة: [العمل مع بيانات السلاسل الزمنية في ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
* المدونة: [دوال النافذة ودوال المصفوفات لتسلسلات commit في Git](https://clickhouse.com/blog/clickhouse-window-array-functions-git-commits)
* المدونة: [إدخال البيانات إلى ClickHouse - الجزء 3 - باستخدام S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3)