Introduction & Overview Types of Window Function in SQL. Explanation with an example using the PostgreSQL. A window function performs a Data Analysis calculation across a set of table rows that are somehow related to the current row. Address the comparable type of calculation can be done with an aggregate function that gives a single row or grouped by condition
Introduction & Overview Types of Window Function in SQL.
A window function performs a Data Analysis calculation across a set of table rows that are somehow related to the current row. Address the comparable type of calculation can be done with an aggregate function that gives a single row or grouped by condition (refer to Figure 1).
Window function does not cause rows to become grouped into a single output row. Rows retain their separate identities also able to access more than just the current row of the query result. (refer to Figure 1)
Figure 1 — Difference between — Aggregated and Windows function
_The database used to explain below concepts: Postgres database and Dataset: Available at [Github Order_Table.csv_](https://github.com/GDhasade/Medium.com_Contents/tree/master/SQL)
Window Function Syntax:
Window_Function([All] expression) OVER( [PARTITION BY expression_list] [ORDER BY order_list Row_or_ Range clause] )
Each part of syntax has been explained as follows:
There are 3 major types of window_functions
Window Aggregated Function: Consist one of the supporting aggregated function i.e. AVG(), COUNT(), MIN(), MAX(), SUM().
Window Aggregated Function
*2. Window Ranking Aggregated Function: *Consist one of the supporting ranking function i.e. RANK(), DENSE_RANK(), ROW_NUMBER().
Window Ranking Aggregated Function
*3. Window Analytical Function: *Consist one of the supporting ranking function i.e. FIRST_VALUE(), LAST_VALUE(), NTH_VALUE().
Window Analytical Function
Over() clause is used to define the partitioning and ordering of rows (i.e. a window) for the functions (i.e. avg, count, etc.) to operate on. Hence, called windows function.
Over() clause have the following parameters:
*For example:- *Rank the product_ids with respect to the product price.
Query: Select product_id, price, Rank() OVER (ORDER BY price desc) AS Average_price From orders;
Parameter — Order by
*2. Partition by:- *Divides the query result set into _partitions _i.e. window function applied to each partition separately.
*For example:- *Calculate the Average_Order_Price by gender i.e. partition by gender.
Parameter — Partition by
The average_price has been calculated by considering partition column i.e. gender. Hence, the same average_price has been calculated for each gender category.
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