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https://github.com/ClickHouse/ClickHouse.git
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Merge branch 'master' into revert-38194-revert-37015-zstd_window_log_max
This commit is contained in:
Alexey Milovidov
commit
61aa0a6234
@ -55,3 +55,372 @@ https://dev.mysql.com/doc/refman/8.0/en/window-function-descriptions.html
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https://dev.mysql.com/doc/refman/8.0/en/window-functions-usage.html
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https://dev.mysql.com/doc/refman/8.0/en/window-functions-frames.html
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## Syntax
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```text
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aggregate_function (column_name)
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OVER ([PARTITION BY groupping_column] [ORDER BY sorting_column]
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[ROWS or RANGE expression_to_bounds_of_frame])
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```
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- `PARTITION BY` - defines how to break a resultset into groups.
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- `ORDER BY` - defines how to order rows inside the group during calculation aggregate_function.
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- `ROWS or RANGE` - defines bounds of a frame, aggregate_function is calculated within a frame.
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```text
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PARTITION
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┌─────────────────┐ <-- UNBOUNDED PRECEDING (BEGINNING of the PARTITION)
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│ │
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│ │
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│=================│ <-- N PRECEDING <─┐
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│ N ROWS │ │ F
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│ Before CURRENT │ │ R
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│~~~~~~~~~~~~~~~~~│ <-- CURRENT ROW │ A
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│ M ROWS │ │ M
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│ After CURRENT │ │ E
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│=================│ <-- M FOLLOWING <─┘
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│ │
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│ │
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└─────────────────┘ <--- UNBOUNDED FOLLOWING (END of the PARTITION)
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```
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## Examples
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```sql
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CREATE TABLE wf_partition
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(
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`part_key` UInt64,
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`value` UInt64
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)
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ENGINE = Memory;
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INSERT INTO wf_partition FORMAT Values
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(1,1,1), (1,2,2), (1,3,3), (2,0,0), (3,0,0);
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key) AS frame_values
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FROM wf_partition
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1,2,3] │ <┐
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│ 1 │ 2 │ 2 │ [1,2,3] │ │ 1-st group
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│ 1 │ 3 │ 3 │ [1,2,3] │ <┘
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│ 2 │ 0 │ 0 │ [0] │ <- 2-nd group
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│ 3 │ 0 │ 0 │ [0] │ <- 3-d group
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└──────────┴───────┴───────┴──────────────┘
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```
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```sql
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CREATE TABLE wf_frame
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(
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`part_key` UInt64,
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`value` UInt64,
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`order` UInt64
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)
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ENGINE = Memory;
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INSERT INTO wf_frame FORMAT Values
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(1,1,1), (1,2,2), (1,3,3), (1,4,4), (1,5,5);
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-- frame is bounded by bounds of a partition (BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC
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Rows BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1,2,3,4,5] │
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│ 1 │ 2 │ 2 │ [1,2,3,4,5] │
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│ 1 │ 3 │ 3 │ [1,2,3,4,5] │
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│ 1 │ 4 │ 4 │ [1,2,3,4,5] │
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│ 1 │ 5 │ 5 │ [1,2,3,4,5] │
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└──────────┴───────┴───────┴──────────────┘
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-- short form - no bound expression, no order by
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1,2,3,4,5] │
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│ 1 │ 2 │ 2 │ [1,2,3,4,5] │
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│ 1 │ 3 │ 3 │ [1,2,3,4,5] │
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│ 1 │ 4 │ 4 │ [1,2,3,4,5] │
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│ 1 │ 5 │ 5 │ [1,2,3,4,5] │
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└──────────┴───────┴───────┴──────────────┘
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-- frame is bounded by the beggining of a partition and the current row
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC
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Rows BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1] │
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│ 1 │ 2 │ 2 │ [1,2] │
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│ 1 │ 3 │ 3 │ [1,2,3] │
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│ 1 │ 4 │ 4 │ [1,2,3,4] │
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│ 1 │ 5 │ 5 │ [1,2,3,4,5] │
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└──────────┴───────┴───────┴──────────────┘
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-- short form (frame is bounded by the beggining of a partition and the current row)
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1] │
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│ 1 │ 2 │ 2 │ [1,2] │
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│ 1 │ 3 │ 3 │ [1,2,3] │
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│ 1 │ 4 │ 4 │ [1,2,3,4] │
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│ 1 │ 5 │ 5 │ [1,2,3,4,5] │
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└──────────┴───────┴───────┴──────────────┘
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-- frame is bounded by the beggining of a partition and the current row, but order is backward
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order DESC) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [5,4,3,2,1] │
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│ 1 │ 2 │ 2 │ [5,4,3,2] │
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│ 1 │ 3 │ 3 │ [5,4,3] │
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│ 1 │ 4 │ 4 │ [5,4] │
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│ 1 │ 5 │ 5 │ [5] │
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└──────────┴───────┴───────┴──────────────┘
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-- sliding frame - 1 PRECEDING ROW AND CURRENT ROW
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC
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Rows BETWEEN 1 PRECEDING AND CURRENT ROW) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1] │
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│ 1 │ 2 │ 2 │ [1,2] │
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│ 1 │ 3 │ 3 │ [2,3] │
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│ 1 │ 4 │ 4 │ [3,4] │
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│ 1 │ 5 │ 5 │ [4,5] │
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└──────────┴───────┴───────┴──────────────┘
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-- sliding frame - Rows BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING
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SELECT
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part_key,
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value,
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order,
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groupArray(value) OVER (PARTITION BY part_key ORDER BY order ASC
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Rows BETWEEN 1 PRECEDING AND UNBOUNDED FOLLOWING) AS frame_values
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FROM wf_frame
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ORDER BY
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part_key ASC,
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value ASC;
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┌─part_key─┬─value─┬─order─┬─frame_values─┐
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│ 1 │ 1 │ 1 │ [1,2,3,4,5] │
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│ 1 │ 2 │ 2 │ [1,2,3,4,5] │
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│ 1 │ 3 │ 3 │ [2,3,4,5] │
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│ 1 │ 4 │ 4 │ [3,4,5] │
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│ 1 │ 5 │ 5 │ [4,5] │
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└──────────┴───────┴───────┴──────────────┘
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```
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## Real world examples
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### Maximum/total salary per department.
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```sql
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CREATE TABLE employees
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(
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`department` String,
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`employee_name` String,
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`salary` Float
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)
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ENGINE = Memory;
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INSERT INTO employees FORMAT Values
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('Finance', 'Jonh', 200),
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('Finance', 'Joan', 210),
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('Finance', 'Jean', 505),
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('IT', 'Tim', 200),
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('IT', 'Anna', 300),
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('IT', 'Elen', 500);
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SELECT
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department,
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employee_name AS emp,
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salary,
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max_salary_per_dep,
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total_salary_per_dep,
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round((salary / total_salary_per_dep) * 100, 2) AS `share_per_dep(%)`
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FROM
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(
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SELECT
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department,
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employee_name,
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salary,
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max(salary) OVER wndw AS max_salary_per_dep,
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sum(salary) OVER wndw AS total_salary_per_dep
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FROM employees
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WINDOW wndw AS (PARTITION BY department
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rows BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
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ORDER BY
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department ASC,
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employee_name ASC
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);
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┌─department─┬─emp──┬─salary─┬─max_salary_per_dep─┬─total_salary_per_dep─┬─share_per_dep(%)─┐
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│ Finance │ Jean │ 505 │ 505 │ 915 │ 55.19 │
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│ Finance │ Joan │ 210 │ 505 │ 915 │ 22.95 │
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│ Finance │ Jonh │ 200 │ 505 │ 915 │ 21.86 │
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│ IT │ Anna │ 300 │ 500 │ 1000 │ 30 │
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│ IT │ Elen │ 500 │ 500 │ 1000 │ 50 │
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│ IT │ Tim │ 200 │ 500 │ 1000 │ 20 │
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└────────────┴──────┴────────┴────────────────────┴──────────────────────┴──────────────────┘
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```
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### Cumulative sum.
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```sql
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CREATE TABLE events
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(
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`metric` String,
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`ts` DateTime,
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`value` Float
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)
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ENGINE = Memory
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INSERT INTO warehouse VALUES
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('sku38', '2020-01-01', 9),
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('sku38', '2020-02-01', 1),
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('sku38', '2020-03-01', -4),
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('sku1', '2020-01-01', 1),
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('sku1', '2020-02-01', 1),
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('sku1', '2020-03-01', 1);
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SELECT
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item,
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ts,
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value,
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sum(value) OVER (PARTITION BY item ORDER BY ts ASC) AS stock_balance
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FROM warehouse
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ORDER BY
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item ASC,
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ts ASC;
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┌─item──┬──────────────────ts─┬─value─┬─stock_balance─┐
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│ sku1 │ 2020-01-01 00:00:00 │ 1 │ 1 │
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│ sku1 │ 2020-02-01 00:00:00 │ 1 │ 2 │
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│ sku1 │ 2020-03-01 00:00:00 │ 1 │ 3 │
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│ sku38 │ 2020-01-01 00:00:00 │ 9 │ 9 │
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│ sku38 │ 2020-02-01 00:00:00 │ 1 │ 10 │
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│ sku38 │ 2020-03-01 00:00:00 │ -4 │ 6 │
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└───────┴─────────────────────┴───────┴───────────────┘
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```
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### Moving / Sliding Average (per 3 rows)
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```sql
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CREATE TABLE sensors
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(
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`metric` String,
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`ts` DateTime,
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`value` Float
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)
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ENGINE = Memory;
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insert into sensors values('cpu_temp', '2020-01-01 00:00:00', 87),
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('cpu_temp', '2020-01-01 00:00:01', 77),
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('cpu_temp', '2020-01-01 00:00:02', 93),
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('cpu_temp', '2020-01-01 00:00:03', 87),
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('cpu_temp', '2020-01-01 00:00:04', 87),
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('cpu_temp', '2020-01-01 00:00:05', 87),
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('cpu_temp', '2020-01-01 00:00:06', 87),
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('cpu_temp', '2020-01-01 00:00:07', 87);
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SELECT
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metric,
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ts,
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value,
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avg(value) OVER
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(PARTITION BY metric ORDER BY ts ASC Rows BETWEEN 2 PRECEDING AND CURRENT ROW)
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AS moving_avg_temp
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FROM sensors
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ORDER BY
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metric ASC,
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ts ASC;
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┌─metric───┬──────────────────ts─┬─value─┬───moving_avg_temp─┐
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│ cpu_temp │ 2020-01-01 00:00:00 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:00:01 │ 77 │ 82 │
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│ cpu_temp │ 2020-01-01 00:00:02 │ 93 │ 85.66666666666667 │
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│ cpu_temp │ 2020-01-01 00:00:03 │ 87 │ 85.66666666666667 │
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│ cpu_temp │ 2020-01-01 00:00:04 │ 87 │ 89 │
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│ cpu_temp │ 2020-01-01 00:00:05 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:00:06 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:00:07 │ 87 │ 87 │
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└──────────┴─────────────────────┴───────┴───────────────────┘
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```
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### Moving / Sliding Average (per 10 seconds)
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```sql
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SELECT
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metric,
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ts,
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value,
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avg(value) OVER (PARTITION BY metric ORDER BY ts
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Range BETWEEN 10 PRECEDING AND CURRENT ROW) AS moving_avg_10_seconds_temp
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FROM sensors
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ORDER BY
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metric ASC,
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ts ASC;
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┌─metric───┬──────────────────ts─┬─value─┬─moving_avg_10_seconds_temp─┐
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│ cpu_temp │ 2020-01-01 00:00:00 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:01:10 │ 77 │ 77 │
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│ cpu_temp │ 2020-01-01 00:02:20 │ 93 │ 93 │
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│ cpu_temp │ 2020-01-01 00:03:30 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:04:40 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:05:50 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:06:00 │ 87 │ 87 │
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│ cpu_temp │ 2020-01-01 00:07:10 │ 87 │ 87 │
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└──────────┴─────────────────────┴───────┴────────────────────────────┘
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```
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||||
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