Big Data Management User Guide

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Aggregate Function as Window Function Example

You work for a human resources group and you want to compare each of your employees' salaries with the average salary in his or her department:

The following table lists the department names, the employee identification number, and the employee's salary:

Department	Employee	Salary
Development	11	5200
Development	7	4200
Development	9	4500
Development	8	6000
Development	10	5200
Personnel	5	3500
Personnel	2	3900
Sales	3	4800
Sales	1	5000
Sales	4	4800

You set an unbounded frame to include all employees in the calculation, and you define an aggregate function to calculate the difference between each employee's salary and the average salary in the department.

Windowing Properties

You define the following windowing properties on the Windowing tab:

Property	Description
Order key	Salary Ascending . Arranges the data by increasing salary.
Partition key	Department . Groups the rows according to department.
Start offset	All Rows Preceding
End offset	All Rows Following

With an unbounded frame, the aggregate function includes all partition rows in the calculation.

For example, suppose the current row is the third row. The third row is in the "Development" partition, so the frame includes the third row in addition to all rows before and after the third row in the "Development" partition.

On the Windowing tab, ascending salary is selected as the order key and department is selected as the partition key. The frame is All Rows Preceding and All Rows Following.

Window Function

An aggregate function acts as a window function when you configure the transformation for windowing.

You define the following aggregate function to calculate the difference between each employee's salary and the average salary in his or her department:

Salary - AVG ( Salary ) = Salary_Diff

Output

The transformation produces the following salary differences:

Department	Employee	Salary	Salary_Diff
Development	11	5200	-820
Development	7	4200	-520
Development	9	4500	180
Development	8	6000	180
Development	10	5200	980
Personnel	5	3500	200
Personnel	2	3900	200
Sales	3	4800	-66
Sales	1	5000	-66
Sales	4	4800	134

You can identify which employees are making less or more than the average salary for his or her department. Based on this information, you can add other transformations to learn more about your data. For example, you might add a Rank transformation to produce a numerical rank for each employee within his or her department.

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Table of Contents

Big Data Management User Guide

Big Data Management User Guide

Aggregate Function as Window Function Example

Aggregate Function as Window Function Example

Windowing Properties

Window Function

Output