soumyava commented on code in PR #14739: URL: https://github.com/apache/druid/pull/14739#discussion_r1381164553
########## docs/querying/sql-window-functions.md: ########## @@ -0,0 +1,227 @@ +--- +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. +Window functions are an [experimental](../development/experimental.md) feature. +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 pefroming 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 a window to the SUM() function to calculate the cumulative changes to a channel over time: Review Comment: Might rephrase as applying the sum() function over the values in the window -- This is an automated message from the Apache Git Service. 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