Working with Aggregate Functions In Apache Age and Its Sample Use Case
Tags: apacheage, database, graphdatabase, datamanagement
Published: 2023-10-19 14:54:36

What are Aggregate Functions

Aggregate functions are a powerful tool for performing calculations on data in graph databases.They allow you to perform calculations on groups of nodes or relationships, and return a single value. This can be useful for tasks such as:

1. Counting the number of nodes in a graph
2. Finding the average or sum of a property across all nodes or relationships
3. Finding the most popular nodes or relationships in a graph
4. Identifying groups of nodes or relationships that are similar to each other

Just to name a few.

In this article we are going to discuss and understand  the various aggregate functions Apache Age supports and how they can be used in sample use cases.

To work with aggregate functions in Apache Age we use the cypher() function to execute cypher queries .

AGE supports the following aggregate functions and they include:

- count()- Returns the number of rows in the group.
- max()-Returns the largest value in the group.
- min()- Returns the smallest value in the group.
- sum()-Returns the sum of the values in the group.
- avg()-Returns the average of the values in the group.
- stDev()-Returns the standard deviation of the values in the group.
- stDevP()-returns the standard deviation for the given value over a group. It uses a standard two-pass method, with N as the denominator, and should be used when calculating the standard deviation for an entire population. 

Here are sample example of the usage of aggregate functions in Apache Age.

**Examples**
In this article we are going to use employees department in an organization as our use case and  illustrate some of the actions that can be performed on employees department data  using aggregate functions

1. **Count()**

_use case :how to calculate no of employees in each department_

```
select * from cypher('employees',$$ 
MATCH (e:Employee)
GROUP BY e.department
RETURN count(e) 
$$)as(employee_count agtype);
```
This query counts the number of employees in each department in the employees database.

2.**AVG()**

_use case:How to calculate the average salary for each department_

```
select *from cypher('employees',$$
MATCH (e:Employee)
GROUP BY e.department
RETURN AVG(e.salary) 
$$) as (average_salary agtype);

```
This query finds the average salary for each department in the employees database. 

3.**MAX()**
use case:How to find the employee with the highest salary in each department.

```
select *from cypher('employees', $$
MATCH (e:Employee)
GROUP BY e.department
RETURN MAX(e.salary)
$$) as (highest_salary agtype);

```
This query finds the maximum salary for each department in the employees database. 

**Sample use cases of how aggregate functions can be used in Apache Age:**

**Making comparisons between different groups of data**: Aggregate functions can be used to make comparisons between different groups of data. For example, you could use aggregate functions to compare the average salary of employees in different departments, the average sales of products in different regions, or the average customer satisfaction scores for different products.

**Summarizing financial data**: Aggregate functions can be used to summarize financial data such as revenue, expenses, and profits. This data can then be used to create reports and dashboards that provide insights into the financial performance of a business. For example, you could use aggregate functions to calculate the total revenue generated in a month, the top expenses incurred in a quarter, and the net profit earned in a year.

**Identifying trends in customer behavior**: You can use aggregate functions to track customer metrics such as purchase history, website visits, and product reviews. This data can then be used to identify trends and patterns in customer behavior. For example, you could use aggregate functions to identify which products are most popular, which customers are most likely to make repeat purchases, and which marketing campaigns are most effective.


**Conclusion**
By understanding how to use aggregate functions, you can extract valuable insights from your data and make better decisions for your organization, businesses and research.