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| --- | ||
| id: sql-window-functions | ||
| title: Window functions | ||
| --- | ||
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| <!-- | ||
| ~ 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. | ||
| --> | ||
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| :::info | ||
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| Apache Druid supports two query languages: [Druid SQL](sql.md) and [native queries](querying.md). | ||
| This document describes the SQL language. | ||
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| 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. | ||
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| Set the context parameter `enableWindowing: true` to use window functions. | ||
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| ::: | ||
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| 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. | ||
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| 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. | ||
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| 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. | ||
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| ```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) | ||
| ``` | ||
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| <details> | ||
| <summary> View results </summary> | ||
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| | `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 | | ||
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| </details> | ||
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| Window functions are similar to [aggregation functions](./aggregations.md). | ||
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| 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. | ||
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| Window functions support aliasing. | ||
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| ## Define a window with the OVER clause | ||
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| 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. | ||
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| :::note | ||
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| 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. | ||
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| ::: | ||
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| The following OVER clause example sets the window dimension to `channel` and orders the results by the absolute value of `delta` ascending: | ||
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| ```sql | ||
| ... | ||
| RANK() OVER (PARTITION BY channel ORDER BY ABS(delta) ASC) | ||
| ... | ||
| ``` | ||
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| ## Window function reference | ||
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| |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 | | ||
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| ## Examples | ||
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| 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. | ||
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| ```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) | ||
| ``` | ||
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| <details> | ||
| <summary> View results </summary> | ||
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| |`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| | ||
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| </details> | ||
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| The following example demonstrates applying the SUM() function over the values in a window to calculate the cumulative changes to a channel over time: | ||
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| ```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 | ||
| ``` | ||
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| <details> | ||
| <summary> View results </summary> | ||
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| |`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| | ||
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| </details> | ||
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| ## Known issues | ||
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| The following are known issues with window functions: | ||
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| - 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 |
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| Original file line number | Diff line number | Diff line change |
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@@ -49,4 +49,9 @@ default long getJitterMills() | |
| { | ||
| return 0; | ||
| } | ||
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| default long getLoadTimeoutMills() | ||
| { | ||
| return 60 * 1000; | ||
| } | ||
| } | ||
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