techdocsmith commented on code in PR #14739: URL: https://github.com/apache/druid/pull/14739#discussion_r1379128294
########## docs/querying/sql-window-functions.md: ########## @@ -0,0 +1,218 @@ +--- +id: sql-window-functions +title: Window functions +--- + +<!-- + ~ Licensed to the Apache Software Foundation (ASF) under one + ~ or more contributor license agreements. See the NOTICE file + ~ distributed with this work for additional information + ~ regarding copyright ownership. The ASF licenses this file + ~ to you under the Apache License, Version 2.0 (the + ~ License); you may not use this file except in compliance + ~ with the License. You may obtain a copy of the License at + ~ + ~ http://www.apache.org/licenses/LICENSE-2.0 + ~ + ~ Unless required by applicable law or agreed to in writing, + ~ software distributed under the License is distributed on an + ~ AS IS BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + ~ KIND, either express or implied. See the License for the + ~ specific language governing permissions and limitations + ~ under the License. + --> + +:::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. + +There are known issues where ORDER BY only works on ascending order and certain options may cause errors. + +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 groups 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. + +```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. Currently only ascending order, ASC, works. + +:::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 a window to the SUM() function 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: + +- Descending order, DESC, in the ORDER BY clause in the window definition causes a Null Pointer Exception. Review Comment: ```suggestion - Aggregates with ORDER BY specified are processed in the window: ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW This is different than 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 ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
