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feat(sql): Add Window aggregate and Ranking functions to SQL reference index

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Jason Stirnaman 2025-02-24 13:37:15 -06:00
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@ -582,6 +582,58 @@ FROM "h2o_feet"
GROUP BY "location"
```
### Window aggregate functions
Window functions let you calculate running totals, moving averages, or other
aggregate-like results without collapsing rows into groups
(unlike non-window aggregate functions).
Window aggregate functions include **all [aggregate functions](#aggregate-functions/)**
and the [ranking functions](#ranking-functions).
The SQL `OVER` clause syntactically distinguishes a window
function from a non-window or aggregate function and defines how to group and
order rows for the window operation.
#### Examples:
{{% influxdb/custom-timestamps %}}
```sql
SELECT
time,
room,
temp,
avg(temp) OVER (PARTITION BY room) AS avg_room_temp
FROM
home
WHERE
time >= '2022-01-01T08:00:00Z'
AND time <= '2022-01-01T09:00:00Z'
ORDER BY
room,
time
```
| time | room | temp | avg_room_temp |
| :------------------ | :---------- | ---: | ------------: |
| 2022-01-01T08:00:00 | Kitchen | 21.0 | 22.0 |
| 2022-01-01T09:00:00 | Kitchen | 23.0 | 22.0 |
| 2022-01-01T08:00:00 | Living Room | 21.1 | 21.25 |
| 2022-01-01T09:00:00 | Living Room | 21.4 | 21.25 |
{{% /influxdb/custom-timestamps %}}
#### Ranking Functions
| Function | Description |
| :------- | :--------------------------------------------------------- |
| CUME_DIST() | Returns the cumulative distribution of a value within a group of values |
| DENSE_RANK() | Returns a rank for each row without gaps in the numbering |
| NTILE() | Distributes the rows in an ordered partition into the specified number of groups |
| PERCENT_RANK() | Returns the percentage rank of the current row within its partition |
| RANK() | Returns the rank of the current row in its partition, allowing gaps between ranks |
| ROW_NUMBER() | Returns the position of the current row in its partition |
### Selector functions
Selector functions are unique to InfluxDB. They behave like aggregate functions in that they take a row of data and compute it down to a single value. However, selectors are unique in that they return a **time value** in addition to the computed value. In short, selectors return an aggregated value along with a timestamp.