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window functions docs (#14739) * draft window functions * Apply suggestions from code review Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> * address comments * remove default column * Update docs/querying/sql-window-functions.md Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> * Update docs/querying/sql-window-functions.md Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> * fix ntile * remove default header column * code tics to remove spelling errors * add known issues, add SUM example * Apply suggestions from code review Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com> * address spelling * remove extra chars * add to sidebar, fix admonition * Update sql-window-functions.md accept suggestion, change admonition style * update sidebar * Delete Untitled.ipynb rm unwanted file * Update docs/querying/sql-window-functions.md * Update docs/querying/sql-window-functions.md * update context param, accept suggestions * accept suggestions * Apply suggestions from code review * Fix known issues * require GROUP BY, explain order of operation * accept suggestions * fix spelling --------- Co-authored-by: Victoria Lim <vtlim@users.noreply.github.com>
2023-11-06 14:34:42 -05:00
---
id: sql-window-functions
title: Window functions
---
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:::info
Apache Druid supports two query languages: [Druid SQL](sql.md) and [native queries](querying.md).
This document describes the SQL language.
Window functions are an [experimental](../development/experimental.md) feature.
Development and testing are still at early stage. Feel free to try window functions and provide your feedback.
Windows functions are not currently supported by multi-stage-query engine so you cannot use them in SQL-based ingestion.
Set the context parameter `enableWindowing: true` to use window functions.
:::
Window functions in Apache Druid produce values based upon the relationship of one row within a window of rows to the other rows within the same window. A window is a group of related rows within a result set. For example, rows with the same value for a specific dimension.
The following example organizes results with the same `channel` value into windows. For each window, the query returns the rank of each row in ascending order based upon its `delta` value.
Window functions in Druid require a GROUP BY statement. Druid performs the row-level aggregations for the GROUP BY before performing the window function calculations.
```sql
SELECT FLOOR(__time TO DAY) AS event_time,
channel,
ABS(delta) AS change,
RANK() OVER w AS rank_value
FROM wikipedia
WHERE channel in ('#kk.wikipedia', '#lt.wikipedia')
AND '2016-06-28' > FLOOR(__time TO DAY) > '2016-06-26'
GROUP BY channel, ABS(delta), __time
WINDOW w AS (PARTITION BY channel ORDER BY ABS(delta) ASC)
```
<details>
<summary> View results </summary>
| `event_time` | `channel` | `change`| `rank_value` |
| -- | -- | -- | -- |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 1 | 1 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 1 | 1 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 7 | 3 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 56 | 4 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 56 | 4 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 63 | 6 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 91 | 7 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 2440 | 8 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 2703 | 9 |
| `2016-06-27T00:00:00.000Z`| `#kk.wikipedia`| 6900 |10 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 1 | 1 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 2 | 2 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 13 | 3 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 28 | 4 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 53 | 5 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 56 | 6 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 59 | 7 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 391 | 8 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 894 | 9 |
| `2016-06-27T00:00:00.000Z`| `#lt.wikipedia`| 4358 | 10 |
</details>
Window functions are similar to [aggregation functions](./aggregations.md).
You can use the OVER clause to treat other Druid aggregation functions as window functions. For example, the sum of a value for rows within a window.
Window functions support aliasing.
## Define a window with the OVER clause
The OVER clause defines the query windows for window functions as follows:
- PARTITION BY indicates the dimension that defines the rows within the window
- ORDER BY specifies the order of the rows within the windows.
:::note
Sometimes windows are called partitions. However, the partitioning for window functions are a shuffle (partition) of the result set created at query time and is not to be confused with Druid's segment partitioning feature which partitions data at ingest time.
:::
The following OVER clause example sets the window dimension to `channel` and orders the results by the absolute value of `delta` ascending:
```sql
...
RANK() OVER (PARTITION BY channel ORDER BY ABS(delta) ASC)
...
```
## Window function reference
|Function|Notes|
|--------|-----|
| `ROW_NUMBER()`| Returns the number of the row within the window |
|`RANK()`| Returns the rank for a row within a window |
|`DENSE_RANK()`| Returns the rank for a row within a window without gaps. For example, if two rows tie for rank of 1, the subsequent row is ranked 2. |
|`PERCENT_RANK()`| Returns the rank of the row calculated as a percentage according to the formula: `(rank - 1) / (total window rows - 1)` |
|`CUME_DIST()`| Returns the cumulative distribution of the current row within the window calculated as `number of window rows at the same rank or higher than current row` / `total window rows` |
|`NTILE(tiles)`| Divides the rows within a window as evenly as possible into the number of tiles, also called buckets, and returns the value of the tile that the row falls into | None |
|`LAG(expr[, offset])`| Returns the value evaluated at the row that precedes the current row by the offset number within the window. `offset` defaults to 1 if not provided |
|`LEAD(expr[, offset])`| Returns the value evaluated at the row that follows the current row by the offset number within the window; if there is no such row, returns the given default value. `offset` defaults to 1 if not provided |
|`FIRST_VALUE(expr)`| Returns the value for the expression for the first row within the window |
|`LAST_VALUE(expr)`| Returns the value for the expression for the last row within the window |
## Examples
The following example illustrates all of the built-in window functions to compare the number of characters changed per event for a channel in the Wikipedia data set.
```sql
SELECT FLOOR(__time TO DAY) AS event_time,
channel,
ABS(delta) AS change,
ROW_NUMBER() OVER w AS row_no,
RANK() OVER w AS rank_no,
DENSE_RANK() OVER w AS dense_rank_no,
PERCENT_RANK() OVER w AS pct_rank,
CUME_DIST() OVER w AS cumulative_dist,
NTILE(4) OVER w AS ntile_val,
LAG(ABS(delta), 1, 0) OVER w AS lag_val,
LEAD(ABS(delta), 1, 0) OVER w AS lead_val,
FIRST_VALUE(ABS(delta)) OVER w AS first_val,
LAST_VALUE(ABS(delta)) OVER w AS last_val
FROM wikipedia
WHERE channel IN ('#kk.wikipedia', '#lt.wikipedia')
GROUP BY channel, ABS(delta), FLOOR(__time TO DAY)
WINDOW w AS (PARTITION BY channel ORDER BY ABS(delta) ASC)
```
<details>
<summary> View results </summary>
|`event_time`|`channel`|`change`|`row_no`|`rank_no`|`dense_rank_no`|`pct_rank`|`cumulative_dist`|`ntile_val`|`lag_val`|`lead_val`|`first_val`|`last_val`|
|------------|---------|--------|--------|---------|---------------|----------|----------------|-----------|---------|----------|-----------|----------|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|1|1|1|1|0.0|0.125|1|null|7|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|7|2|2|2|0.14285714285714285|0.25|1|1|56|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|56|3|3|3|0.2857142857142857|0.375|2|7|63|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|63|4|4|4|0.42857142857142855|0.5|2|56|91|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|91|5|5|5|0.5714285714285714|0.625|3|63|2440|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|2440|6|6|6|0.7142857142857143|0.75|3|91|2703|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|2703|7|7|7|0.8571428571428571|0.875|4|2440|6900|1|6900|
|`2016-06-27T00:00:00.000Z`|`#kk.wikipedia`|6900|8|8|8|1|1|4|2703|null|1|6900|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|1|1|1|1|0|0.1|1|null|2|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|2|2|2|2|0.1111111111111111|0.2|1|1|13|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|13|3|3|3|0.2222222222222222|0.3|1|2|28|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|28|4|4|4|0.3333333333333333|0.4|2|13|53|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|53|5|5|5|0.4444444444444444|0.5|2|28|56|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|56|6|6|6|0.5555555555555556|0.6|2|53|59|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|59|7|7|7|0.6666666666666666|0.7|3|56|391|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|391|8|8|8|0.7777777777777778|0.8|3|59|894|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|894|9|9|9|0.8888888888888888|0.9|4|391|4358|1|4358|
|`2016-06-27T00:00:00.000Z`| `#lt.wikipedia`|4358|10|10|10|1|1|4|894|null|1|4358|
</details>
The following example demonstrates applying the SUM() function over the values in a window to calculate the cumulative changes to a channel over time:
```sql
SELECT
FLOOR(__time TO MINUTE) as "time",
channel,
ABS(delta) AS changes,
sum(ABS(delta)) OVER (PARTITION BY channel ORDER BY FLOOR(__time TO MINUTE) ASC) AS cum_changes
FROM wikipedia
WHERE channel IN ('#kk.wikipedia', '#lt.wikipedia')
GROUP BY channel, __time, delta
```
<details>
<summary> View results </summary>
|`time`|`channel`|`changes`|`cum_changes`|
|------|---------|---------|-------------|
|`2016-06-27T04:20:00.000Z`|`#kk.wikipedia`|56|56|
|`2016-06-27T04:35:00.000Z`|`#kk.wikipedia`|2440|2496|
|`2016-06-27T06:15:00.000Z`|`#kk.wikipedia`|91|2587|
|`2016-06-27T07:32:00.000Z`|`#kk.wikipedia`|1|2588|
|`2016-06-27T09:00:00.000Z`|`#kk.wikipedia`|2703|5291|
|`2016-06-27T09:24:00.000Z`|`#kk.wikipedia`|1|5292|
|`2016-06-27T11:00:00.000Z`|`#kk.wikipedia`|63|5355|
|`2016-06-27T11:05:00.000Z`|`#kk.wikipedia`|7|5362|
|`2016-06-27T11:32:00.000Z`|`#kk.wikipedia`|56|5418|
|`2016-06-27T15:21:00.000Z`|`#kk.wikipedia`|6900|12318|
|`2016-06-27T06:17:00.000Z`|`#lt.wikipedia`|2|2|
|`2016-06-27T07:55:00.000Z`|`#lt.wikipedia`|13|15|
|`2016-06-27T09:05:00.000Z`|`#lt.wikipedia`|894|909|
|`2016-06-27T09:12:00.000Z`|`#lt.wikipedia`|391|1300|
|`2016-06-27T09:23:00.000Z`|`#lt.wikipedia`|56|1356|
|`2016-06-27T10:59:00.000Z`|`#lt.wikipedia`|1|1357|
|`2016-06-27T11:49:00.000Z`|`#lt.wikipedia`|59|1416|
|`2016-06-27T12:41:00.000Z`|`#lt.wikipedia`|53|1469|
|`2016-06-27T12:58:00.000Z`|`#lt.wikipedia`|28|1497|
|`2016-06-27T19:03:00.000Z`|`#lt.wikipedia`|4358|5855|
</details>
## Known issues
The following are known issues with window functions:
- Aggregates with ORDER BY specified are processed in the window: ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
This behavior differs from other databases that use the default of RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
In cases where the order column is unique there is no difference between RANGE / ROWS; windows with RANGE specifications are handled as ROWS.
- LEAD/LAG ignores the default value
- LAST_VALUE returns the last value of the window even when you include an ORDER BY clause