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docs/en/sql-reference/window-functions/dense_rank.md Normal file
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---
slug: /en/sql-reference/window-functions/dense_rank
sidebar_label: dense_rank
sidebar_position: 2
---
# dense_rank
This window function ranks the current row within its partition without gaps. In other words, if the value of any new row encountered is equal to the value of one of the previous rows then it will receive the next successive rank without any gaps in ranking.
The [rank](./rank.md) function provides the same behaviour, but with gaps in ranking.
**Syntax**
```sql
dense_rank (column_name)
OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
[ROWS or RANGE expression_to_bound_rows_withing_the_group]] | [window_name])
FROM table_name
WINDOW window_name as ([[PARTITION BY grouping_column] [ORDER BY sorting_column])
```
For more detail on window function syntax see: [Window Functions - Syntax](./index.md/#syntax).
**Returned value**
- A number for the current row within its partition, without gaps in ranking. [UInt64](../data-types/int-uint.md).
**Example**
The following example is based on the example provided in the video instructional [Ranking window functions in ClickHouse](https://youtu.be/Yku9mmBYm_4?si=XIMu1jpYucCQEoXA).
Query:
```sql
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'),
('South Hampton Seagulls', 'Douglas Benson', 150000, 'M'),
('South Hampton Seagulls', 'James Henderson', 140000, 'M');
```
```sql
SELECT player, salary,
dense_rank() OVER (ORDER BY salary DESC) AS dense_rank
FROM salaries;
```
Result:
```response
┌─player──────────┬─salary─┬─dense_rank─┐
1. │ Gary Chen │ 195000 │ 1 │
2. │ Robert George │ 195000 │ 1 │
3. │ Charles Juarez │ 190000 │ 2 │
4. │ Michael Stanley │ 150000 │ 3 │
5. │ Douglas Benson │ 150000 │ 3 │
6. │ Scott Harrison │ 150000 │ 3 │
7. │ James Henderson │ 140000 │ 4 │
└─────────────────┴────────┴────────────┘
```

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docs/en/sql-reference/window-functions/index.md
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---
slug: /en/sql-reference/window-functions/
sidebar_position: 62
sidebar_label: Window Functions
title: Window Functions
sidebar_position: 1
---
# Window Functions
Windows functions let you perform calculations across a set of rows that are related to the current row.
Some of the calculations that you can do are similar to those that can be done with an aggregate function, but a window function doesn't cause rows to be grouped into a single output - the individual rows are still returned.
@ -12,19 +13,19 @@ Some of the calculations that you can do are similar to those that can be done w
ClickHouse supports the standard grammar for defining windows and window functions. The table below indicates whether a feature is currently supported.
| Feature | Supported? |
|------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Feature | Supported? |
|--------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ad hoc window specification (`count(*) over (partition by id order by time desc)`) | ✅ |
| expressions involving window functions, e.g. `(count(*) over ()) / 2)` | ✅ |
| `WINDOW` clause (`select ... from table window w as (partition by id)`) | ✅ |
| `ROWS` frame | ✅ |
| `RANGE` frame | ✅ (the default) |
| `INTERVAL` syntax for `DateTime` `RANGE OFFSET` frame | ❌ (specify the number of seconds instead (`RANGE` works with any numeric type).) |
| `GROUPS` frame | ❌ |
| Calculating aggregate functions over a frame (`sum(value) over (order by time)`) | ✅ (All aggregate functions are supported) |
| `rank()`, `dense_rank()`, `row_number()` | ✅ |
| `lag/lead(value, offset)` | ❌ <br/> You can use one of the following workarounds:<br/> 1) `any(value) over (.... rows between <offset> preceding and <offset> preceding)`, or `following` for `lead` <br/> 2) `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` |
| ntile(buckets) | ✅ <br/> Specify window like, (partition by x order by y rows between unbounded preceding and unrounded following). |
| expressions involving window functions, e.g. `(count(*) over ()) / 2)` | ✅ |
| `WINDOW` clause (`select ... from table window w as (partition by id)`) | ✅ |
| `ROWS` frame | ✅ |
| `RANGE` frame | ✅ (the default) |
| `INTERVAL` syntax for `DateTime` `RANGE OFFSET` frame | ❌ (specify the number of seconds instead (`RANGE` works with any numeric type).) |
| `GROUPS` frame | ❌ |
| Calculating aggregate functions over a frame (`sum(value) over (order by time)`) | ✅ (All aggregate functions are supported) |
| `rank()`, `dense_rank()`, `row_number()` | ✅ |
| `lag/lead(value, offset)` | ❌ <br/> You can use one of the following workarounds:<br/> 1) `any(value) over (.... rows between <offset> preceding and <offset> preceding)`, or `following` for `lead` <br/> 2) `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` |
| ntile(buckets) | ✅ <br/> Specify window like, (partition by x order by y rows between unbounded preceding and unrounded following). |
## ClickHouse-specific Window Functions
@ -74,12 +75,12 @@ WINDOW window_name as ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
These functions can be used only as a window function.
- `row_number()` - Number the current row within its partition starting from 1.
- [`row_number()`](./row_number.md) - Number the current row within its partition starting from 1.
- `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.
- `rank()` - Rank the current row within its partition with gaps.
- `dense_rank()` - Rank the current row within its partition without gaps.
- [`rank()`](./rank.md) - Rank the current row within its partition with gaps.
- [`dense_rank()`](./dense_rank.md) - 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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---
slug: /en/sql-reference/window-functions/rank
sidebar_label: rank
sidebar_position: 3
---
# rank
This window function ranks the current row within its partition with gaps. In other words, if the value of any row it encounters is equal to the value of a previous row then it will receive the same rank as that previous row.
The rank of the next row is then equal to the rank of the previous row plus a gap equal to the number of times the previous rank was given.
The [dense_rank](./dense_rank.md) function provides the same behaviour but without gaps in ranking.
**Syntax**
```sql
rank (column_name)
OVER ([[PARTITION BY grouping_column] [ORDER BY sorting_column]
[ROWS or RANGE expression_to_bound_rows_withing_the_group]] | [window_name])
FROM table_name
WINDOW window_name as ([[PARTITION BY grouping_column] [ORDER BY sorting_column])
```
For more detail on window function syntax see: [Window Functions - Syntax](./index.md/#syntax).
**Returned value**
- A number for the current row within its partition, including gaps. [UInt64](../data-types/int-uint.md).
**Example**
The following example is based on the example provided in the video instructional [Ranking window functions in ClickHouse](https://youtu.be/Yku9mmBYm_4?si=XIMu1jpYucCQEoXA).
Query:
```sql
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'),
('South Hampton Seagulls', 'Douglas Benson', 150000, 'M'),
('South Hampton Seagulls', 'James Henderson', 140000, 'M');
```
```sql
SELECT player, salary,
rank() OVER (ORDER BY salary DESC) AS rank
FROM salaries;
```
Result:
```response
┌─player──────────┬─salary─┬─rank─┐
1. │ Gary Chen │ 195000 │ 1 │
2. │ Robert George │ 195000 │ 1 │
3. │ Charles Juarez │ 190000 │ 3 │
4. │ Douglas Benson │ 150000 │ 4 │
5. │ Michael Stanley │ 150000 │ 4 │
6. │ Scott Harrison │ 150000 │ 4 │
7. │ James Henderson │ 140000 │ 7 │
└─────────────────┴────────┴──────┘
```

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docs/en/sql-reference/window-functions/row_number.md Normal file
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