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Document window functions: CORE-2090, CORE-2133, CORE-2823, CORE-2830…
…, CORE-2869, CORE-3619, CORE-3620, CORE-3621.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,233 @@ | ||
| ---------------- | ||
| Window Functions | ||
| ---------------- | ||
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| By the SQL specification, window functions (also know as analytical functions) are a kind of | ||
| aggregation, but which does not "filter" the result set of a query. The aggregated data is mixed | ||
| with the query result set. That sort of functions are used with the OVER clause. Window functions | ||
| may appear only in the select list or the order by clause of a query. | ||
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| Additional to the OVER clause, Firebird window functions may be partitioned and ordered. | ||
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| Syntax: | ||
| <window function> ::= <window function name>([<expr> [, <expr> ...]) OVER ( | ||
| [PARTITION BY <expr> [, <expr> ...]] | ||
| [ORDER BY <expr> [<direction>] [<nulls placement>] [, <expr> [<direction>] [<nulls placement>] ...] | ||
| ) | ||
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| <direction> ::= {ASC | DESC} | ||
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| <nulls placement> ::= NULLS {FIRST | LAST} | ||
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| 1. Aggregate functions used as window functions | ||
| ----------------------------------------------- | ||
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| All aggregate functions may be used as window functions, adding the OVER clause. Imagine | ||
| a table EMPLOYEE with columns ID, NAME and SALARY, and the need to show each employee with his | ||
| respective salary and the percentage of his salary over the payroll. With a "normal" query, this is | ||
| possible in the following manner: | ||
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| select | ||
| id, | ||
| department, | ||
| salary, | ||
| salary / (select sum(salary) from employee) percentage | ||
| from employee | ||
| order by id; | ||
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| Results: | ||
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| id department salary percentage | ||
| -- ---------- ------ ---------- | ||
| 1 R & D 10.00 0.2040 | ||
| 2 SALES 12.00 0.2448 | ||
| 3 SALES 8.00 0.1632 | ||
| 4 R & D 9.00 0.1836 | ||
| 5 R & D 10.00 0.2040 | ||
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| It's necessary to repeat the query in a subquery and wait so much to see the results, specially if | ||
| EMPLOYEE is a complex view. | ||
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| The same query could be specified in much more elegant and faster way using a window function: | ||
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| select | ||
| id, | ||
| department, | ||
| salary, | ||
| salary / sum(salary) over () percentage | ||
| from employee | ||
| order by id; | ||
|
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| Here, sum(salary) over () is computed with the sum of all SALARY from the query (the employee table). | ||
|
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| 2. Partitioning | ||
| --------------- | ||
|
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| Like aggregate functions, that may operate alone or in relation to a group, window functions may | ||
| also operate on a group, which is called "partition". Its syntax is: | ||
| <window function>(...) OVER (PARTITION BY <expr> [, <expr> ...]) | ||
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| When aggregation is done over a group, it could produce more than one row. So the result set | ||
| generated by a partition is joined with the main query using the same expression list of the | ||
| partition. | ||
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| Continuing the employee example, instead of get the percentage of the employee salary over all | ||
| employees, we would like to get the percentage based only on the employees in the same department: | ||
|
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||
| select | ||
| id, | ||
| department, | ||
| salary, | ||
| salary / sum(salary) over (partition by department) percentage | ||
| from employee | ||
| order by id; | ||
|
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||
| Results: | ||
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| id department salary percentage | ||
| -- ---------- ------ ---------- | ||
| 1 R & D 10.00 0.3448 | ||
| 2 SALES 12.00 0.6000 | ||
| 3 SALES 8.00 0.4000 | ||
| 4 R & D 9.00 0.3103 | ||
| 5 R & D 10.00 0.3448 | ||
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| 3. Ordering | ||
| ----------- | ||
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| The ORDER BY sub-clause can be used with or without partitions, and used with the standard aggregate | ||
| functions, make them return the partial aggregations as the records are being processed. Example: | ||
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| select | ||
| id, | ||
| salary, | ||
| sum(salary) over (order by salary) cum_salary | ||
| from employee | ||
| order by salary; | ||
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| The result set produced will be: | ||
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| id salary cum_salary | ||
| -- ------ ---------- | ||
| 3 8.00 8.00 | ||
| 4 9.00 17.00 | ||
| 1 10.00 37.00 | ||
| 5 10.00 37.00 | ||
| 2 12.00 49.00 | ||
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| Then cum_salary returns the partial/accumulated (or running) aggregation (of the SUM function). | ||
| It may appear strange that 37.00 is repeated for the ids 1 and 5, but that is how it should work. | ||
| The ORDER BY keys are grouped together and the aggregation is computed once (but summing the two | ||
| 10.00). To avoid this, you can add the ID field to the end of the ORDER BY clause. | ||
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| It's possible to use multiple windows with different orders, and ORDER BY parts like ASC/DESC and | ||
| NULLS FIRST/LAST. | ||
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| With a partition, ORDER BY works the same way, but at each partition boundary the aggregation is | ||
| reset. | ||
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| All aggregation functions are usable with ORDER BY, except the LIST function. | ||
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| 4. Exclusive window functions | ||
| ----------------------------- | ||
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| Beyond aggregate functions, there is also exclusive window functions, currently divided in ranking | ||
| and navigational categories. | ||
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| Both set of functions can be used with/without partition/ordering, but the usage does not make much | ||
| sense without ordering. | ||
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| 4.1 Ranking functions | ||
| --------------------- | ||
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| Syntax: | ||
| <ranking window function> ::= | ||
| DENSE_RANK() | | ||
| RANK() | | ||
| ROW_NUMBER() | ||
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| The rank functions compute the ordinal rank of a row within the window partition. In this category | ||
| is the functions: DENSE_RANK, RANK and ROW_NUMBER. | ||
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| With these functions, one can create different type of incremental counters. Think about | ||
| SUM(1) OVER (ORDER BY SALARY), these functions does this type of thing, but all of them in different | ||
| ways. Following is an example query, also comparing with the SUM behavior. | ||
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| select | ||
| id, | ||
| salary, | ||
| dense_rank() over (order by salary), | ||
| rank() over (order by salary), | ||
| row_number() over (order by salary), | ||
| sum(1) over (order by salary) | ||
| from employee | ||
| order by salary; | ||
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| And the result set: | ||
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| id salary dense_rank rank row_number sum | ||
| -- ------ ---------- ---- ---------- --- | ||
| 3 8.00 1 1 1 1 | ||
| 4 9.00 2 2 2 2 | ||
| 1 10.00 3 3 3 4 | ||
| 5 10.00 3 3 4 4 | ||
| 2 12.00 4 5 5 5 | ||
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| The difference between DENSE_RANK and RANK is that there is a gap related to duplicate rows (in | ||
| relation to the window ordering) only in RANK. DENSE_RANK continues assigning sequential numbers | ||
| after the duplicate salary. On the other hand, ROW_NUMBER always assigns sequential numbers, even | ||
| when there is duplicate values. | ||
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| 4.2 Navigational functions | ||
| -------------------------- | ||
|
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| Syntax: | ||
| <navigational window function> ::= | ||
| FIRST_VALUE(<expr>) | | ||
| LAST_VALUE(<expr>) | | ||
| NTH_VALUE(<expr>, <offset>) [FROM FIRST | FROM LAST] | | ||
| LAG(<expr> [ [, <offset> [, <default> ] ] ) | | ||
| LEAD(<expr> [ [, <offset> [, <default> ] ] ) | ||
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| The navigational functions gets the simple (non-aggregated) value of an expression from another | ||
| row (inside the same partition) of the query. | ||
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| It's important to note that FIRST_VALUE, LAST_VALUE and NTH_VALUE also operates on a window frame, | ||
| and Firebird is currently always framing from the first to the current (and not the last) row of | ||
| the partition. This is likely to get strange results for NTH_VALUE and specially LAST_VALUE. | ||
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| select | ||
| id, | ||
| salary, | ||
| first_value(salary) over (order by salary), | ||
| last_value(salary) over (order by salary), | ||
| nth_value(salary, 2) over (order by salary), | ||
| lag(salary) over (order by salary), | ||
| lead(salary) over (order by salary) | ||
| from employee | ||
| order by salary; | ||
|
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| And the result set: | ||
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| id salary first_value last_value nth_value lag lead | ||
| -- ------ ----------- ---------- --------- ------ ------ | ||
| 3 8.00 8.00 8.00 <null> <null> 9.00 | ||
| 4 9.00 8.00 9.00 9.00 8.00 10.00 | ||
| 1 10.00 8.00 10.00 9.00 9.00 10.00 | ||
| 5 10.00 8.00 10.00 9.00 10.00 12.00 | ||
| 2 12.00 8.00 12.00 9.00 10.00 <null> | ||
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| FIRST_VALUE and LAST_VALUE gets respectively the first and last value of the ordered partition. | ||
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| NTH_VALUE gets the n-th value, starting from the first (default) or the last record, from the | ||
| ordered partition. If offset is 1 from first, it's equivalent to FIRST_VALUE. If offset is 1 from | ||
| last, it's equivalent to LAST_VALUE. | ||
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| LAG and LEAD gets the value within a distance respect to the current row and the offset (which | ||
| defaults to 1) passed. In the case the offset points to outside of the partition, the default | ||
| parameter (which defaults to NULL) is returned. LAG looks for a preceding row, and LEAD for a | ||
| following row. | ||
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| Author: | ||
| Adriano dos Santos Fernandes <adrianosf at gmail.com> |