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ClickHouse/docs/en/sql-reference/window-functions/index.md

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---
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slug: /en/sql-reference/window-functions/
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sidebar_position: 62
sidebar_label: Window Functions
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title: Window Functions
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---
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ClickHouse supports the standard grammar for defining windows and window functions. The following features are currently supported:
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| Feature | Support or workaround |
|------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ad hoc window specification (`count(*) over (partition by id order by time desc)`) | supported |
| expressions involving window functions, e.g. `(count(*) over ()) / 2)` | supported |
| `WINDOW` clause (`select ... from table window w as (partition by id)`) | supported |
| `ROWS` frame | supported |
| `RANGE` frame | supported, the default |
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| `INTERVAL` syntax for `DateTime` `RANGE OFFSET` frame | not supported, specify the number of seconds instead (`RANGE` works with any numeric type). |
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| `GROUPS` frame | not supported |
| Calculating aggregate functions over a frame (`sum(value) over (order by time)`) | all aggregate functions are supported |
| `rank()`, `dense_rank()`, `row_number()` | supported |
| `lag/lead(value, offset)` | Not supported. Workarounds: |
| | 1) replace with `any(value) over (.... rows between <offset> preceding and <offset> preceding)`, or `following` for `lead` |
| | 2) use `lagInFrame/leadInFrame`, which are analogous, but respect the window frame. To get behavior identical to `lag/lead`, use `rows between unbounded preceding and unbounded following` |
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| ntile(buckets) | Supported. Specify window like, (partition by x order by y rows between unbounded preceding and unounded following). |
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## ClickHouse-specific Window Functions
### nonNegativeDerivative(metric_column, timestamp_column[, INTERVAL X UNITS])
Finds non-negative derivative for given `metric_column` by `timestamp_column`.
`INTERVAL` can be omitted, default is `INTERVAL 1 SECOND`.
The computed value is the following for each row:
- `0` for 1st row,
- ${metric_i - metric_{i-1} \over timestamp_i - timestamp_{i-1}} * interval$ for $i_th$ row.
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## References
### GitHub Issues
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The roadmap for the initial support of window functions is [in this issue](https://github.com/ClickHouse/ClickHouse/issues/18097).
All GitHub issues related to window funtions have the [comp-window-functions](https://github.com/ClickHouse/ClickHouse/labels/comp-window-functions) tag.
### Tests
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These tests contain the examples of the currently supported grammar:
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https://github.com/ClickHouse/ClickHouse/blob/master/tests/performance/window_functions.xml
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https://github.com/ClickHouse/ClickHouse/blob/master/tests/queries/0_stateless/01591_window_functions.sql
### Postgres Docs
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https://www.postgresql.org/docs/current/sql-select.html#SQL-WINDOW
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https://www.postgresql.org/docs/devel/sql-expressions.html#SYNTAX-WINDOW-FUNCTIONS
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https://www.postgresql.org/docs/devel/functions-window.html
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https://www.postgresql.org/docs/devel/tutorial-window.html
### MySQL Docs
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https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html
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https://dev.mysql.com/doc/refman/8.0/en/window-functions-usage.html
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https://dev.mysql.com/doc/refman/8.0/en/window-functions-frames.html
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## Syntax
```text
aggregate_function (column_name)
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OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
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[ROWS or RANGE expression_to_bound_rows_withing_the_group]] | [window_name])
FROM table_name
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WINDOW window_name as ([[PARTITION BY grouping_column] [ORDER BY sorting_column])
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```
- `PARTITION BY` - defines how to break a resultset into groups.
- `ORDER BY` - defines how to order rows inside the group during calculation aggregate_function.
- `ROWS or RANGE` - defines bounds of a frame, aggregate_function is calculated within a frame.
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- `WINDOW` - allows to reuse a window definition with multiple exressions.
### Functions
These functions can be used only as a window function.
`row_number()` - Number the current row within its partition starting from 1.
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`first_value(x)` - Return the first non-NULL value evaluated within its ordered frame.
`last_value(x)` - Return the last non-NULL value evaluated within its ordered frame.
`nth_value(x, offset)` - Return the first non-NULL value evaluated against the nth row (offset) in its ordered frame.
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`rank()` - Rank the current row within its partition with gaps.
`dense_rank()` - Rank the current row within its partition without gaps.
`lagInFrame(x)` - Return a value evaluated at the row that is at a specified physical offset row before the current row within the ordered frame.
`leadInFrame(x)` - Return a value evaluated at the row that is offset rows after the current row within the ordered frame.
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```text
PARTITION
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┌─────────────────┐ <-- UNBOUNDED PRECEDING (BEGINNING of the PARTITION)
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│ │
│ │
│=================│ <-- 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)
```
## Examples
```sql
CREATE TABLE wf_partition
(
`part_key` UInt64,
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`value` UInt64,
`order` UInt64
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)
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
└──────────┴───────┴───────┴──────────────┘
```
```sql
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);
-- 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] │
└──────────┴───────┴───────┴──────────────┘
-- short form - no bound expression, no order by
SELECT
part_key,
value,
order,
groupArray(value) OVER (PARTITION BY part_key) 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] │
└──────────┴───────┴───────┴──────────────┘
-- frame is bounded by the beggining 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] │
└──────────┴───────┴───────┴──────────────┘
-- short form (frame is bounded by the beggining of a partition and the current row)
SELECT
part_key,
value,
order,
groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC) 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] │
└──────────┴───────┴───────┴──────────────┘
-- frame is bounded by the beggining 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] │
└──────────┴───────┴───────┴──────────────┘
-- 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] │
└──────────┴───────┴───────┴──────────────┘
-- 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] │
└──────────┴───────┴───────┴──────────────┘
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-- 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 │
└──────────┴───────┴───────┴──────────────┴──────┴──────┴──────┴──────┘
-- 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 │
└────────────────┴───────────────┴──────────────┴────────────────┴───────────────┴──────────────┘
-- 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 │
└────────────────┴──────────────┘
-- 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 │
└────────────────┴──────────────┘
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```
## Real world examples
### Maximum/total salary per department.
```sql
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);
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 │
└────────────┴──────┴────────┴────────────────────┴──────────────────────┴──────────────────┘
```
### Cumulative sum.
```sql
Fix "Cumulative sum" example in "Window functions" Fixes a problem in "Cumulative sum" example in "Window functions" documentation, when CREATE TABLE part of example was not match to SQL examples. see: https://clickhouse.com/docs/en/sql-reference/window-functions/#cumulative-sum
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CREATE TABLE warehouse
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(
Fix "Cumulative sum" example in "Window functions" Fixes a problem in "Cumulative sum" example in "Window functions" documentation, when CREATE TABLE part of example was not match to SQL examples. see: https://clickhouse.com/docs/en/sql-reference/window-functions/#cumulative-sum
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`item` String,
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`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);
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 │
└───────┴─────────────────────┴───────┴───────────────┘
```
### Moving / Sliding Average (per 3 rows)
```sql
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);
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 │
└──────────┴─────────────────────┴───────┴───────────────────┘
```
### Moving / Sliding Average (per 10 seconds)
```sql
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 │
└──────────┴─────────────────────┴───────┴────────────────────────────┘
```
Update index.md
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### Moving / Sliding Average (per 10 days)
Update index.md
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Temperature is stored with second precision, but using `Range` and `ORDER BY toDate(ts)` we form a frame with the size of 10 units, and because of `toDate(ts)` the unit is a day.
Update index.md
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```sql
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);
SELECT
metric,
ts,
value,
round(avg(value) OVER (PARTITION BY metric ORDER BY toDate(ts)
Range BETWEEN 10 PRECEDING AND CURRENT ROW),2) 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 │
└──────────────┴─────────────────────┴───────┴─────────────────────────┘
```
cross link docs to blogs
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## Related Content
link docs to blogs
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- Blog: [Working with time series data in ClickHouse](https://clickhouse.com/blog/working-with-time-series-data-and-functions-ClickHouse)
- Blog: [Window and array functions for Git commit sequences](https://clickhouse.com/blog/clickhouse-window-array-functions-git-commits)
- Blog: [Getting Data Into ClickHouse - Part 3 - Using S3](https://clickhouse.com/blog/getting-data-into-clickhouse-part-3-s3)
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