DbSchema | How to Perform Aggregate Functions in PostgreSQL?

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PostgreSQL • 30-May-2023

by Malik Umair Sajjad

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Table of Contents

• Introduction
• What is an Aggregate Function?
• Advantages and Limitations of Using Aggregate Functions
• Restrictions on Using Aggregate Functions
• Aggregate Functions Overview
• Performing Aggregate Functions in psql and DbSchema
  • Using Aggregate Functions in psql
  • Using Aggregate Functions in DbSchema
• Conclusion
• References

Introduction

In PostgreSQL, aggregate functions allow you to perform calculations on sets of values and return a single result. These functions are commonly used in database queries to summarize or transform data. In this article, we will explore how to use aggregate functions in both psql (PostgreSQL command-line interface) and DbSchema (a graphical database tool).

What is an Aggregate Function?

An aggregate function is a function that operates on a set of values and returns a single value. It takes multiple input values and produces a single output value based on those inputs. Examples of aggregate functions include calculating the average, sum, count, maximum, and minimum values from a dataset.

Advantages and Limitations of Using Aggregate Functions

Advantages:

Using aggregate functions in your queries offers several advantages, such as:

• Simplifying complex calculations: Aggregate functions provide a concise way to perform calculations on a large amount of data.
• Efficient data summarization: They allow you to summarize data quickly without having to retrieve and process every individual record.
• Improved query readability: By using aggregate functions, you can express your queries in a more intuitive and readable manner.

Limitations:

However, there are also some limitations to keep in mind when using aggregate functions:

• Grouping requirements: Many aggregate functions require grouping the data by one or more columns to produce meaningful results.
• *Limited flexibility8: Aggregate functions operate on entire columns or groups, so they may not be suitable for all types of calculations or transformations.
• Performance considerations: When dealing with large datasets, aggregate functions can impact query performance, so optimization techniques may be necessary.

Restrictions on Using Aggregate Functions

While aggregate functions offer powerful capabilities, there are a few restrictions to be aware of:

• Cannot be nested within each other: Aggregate functions cannot be used as arguments for other aggregate functions. However, subqueries can be employed to work around this limitation.
• Ambiguity in column selection: When using aggregate functions with non-aggregated columns, you must specify how those columns should be grouped or aggregated.

Aggregate Functions Overview

Here is a brief explanation of some commonly used aggregate functions:

Aggregate Function	Description
AVG()	Calculates the average of a set of values.
COUNT()	Counts the number of rows in a dataset. Can be used with or without specifying a column.
MIN()	Retrieves the minimum value from a dataset. Can be used with numerical, string, or date/time values.
MAX()	Retrieves the maximum value from a dataset. Can be used with various data types.
SUM()	Calculates the sum of a set of values. Works with numerical data and returns the total sum.

Performing Aggregate Functions in psql and DbSchema

Using Aggregate Functions in psql

To perform aggregate functions in psql, follow these steps:

1. Connect to your PostgreSQL database using psql.
   For installation and establishing connection refer to PostgreSQL-How to create a database?

2. Construct a SELECT statement with the desired aggregate function(s) and column(s).

3. Optionally, use the GROUP BY clause to group the data based on one or more columns.

4. Execute the query to retrieve the aggregated result.

Sample Database:

employee Table:

id	name	age	salary
1	John	25	5000
2	Sarah	28	6000
3	Michael	30	5500
4	Jessica	27	6500
5	William	32	7000

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AVG()

The AVG() function calculates the average of a set of values. It takes a column or an expression as input and returns the average value.

Example:

-- Calculate the average salary of employees
SELECT AVG(salary) AS average_salary FROM employee;

Result from Query:

Following is the result obtained by executing query on the sample database

Average Salary
6000

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COUNT()

The COUNT() function counts the number of rows in a dataset. It can be used with or without specifying a column. When used without a column, it counts all the rows in the result set.

Example:

-- Count the number of employees 
SELECT COUNT(*) AS employee_count FROM employee;

Result from Query:

Following is the result obtained by executing query on the sample database

Count
5

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MIN()

The MIN() function retrieves the minimum value from a dataset. It can be used with numerical, string, or date/time values.

Example:

-- Find the minimum age of employee in the employee table
SELECT MIN(age) AS minimum_age FROM employee;

Result from Query:

Following is the result obtained by executing query on the sample database

Minimum Age
25

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MAX()

The MAX() function retrieves the maximum value from a dataset. It can also be used with various data types.

Example:

-- Find the maximum salary of employee in the employee table
SELECT MAX(salary) AS maximum_salary FROM employee;

Result from Query:

Following is the result obtained by executing query on the sample database

Maximum Salary
7000

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SUM()

The SUM() function calculates the sum of a set of values. It works with numerical data and returns the total sum.

Example:

-- Calculate the total salary of all the employees present in the employee table
SELECT SUM(salary) AS total_salary FROM employee;

Result from Query:

Following is the result obtained by executing query on the sample database

**Total Salary
30000

Using Aggregate Functions in DbSchema

To perform aggregate functions in DbSchema, follow these steps:

1. Launch DbSchema and connect to your PostgreSQL database.
2. Navigate to the SQL editor or query builder interface.
3. Build your query using the graphical tools or write the SQL code directly.
4. Include the desired aggregate function(s) and column(s) in your query.
5. Execute the query to retrieve the aggregated result.

Example:

-- Calculate the total salary of all the employees present in the employee table
SELECT SUM(salary) AS total_salary FROM employee;

-- Calculate the average salary of employees
SELECT AVG(salary) AS average_salary FROM employee;

-- Count the number of employees 
SELECT COUNT(*) AS employee_count FROM employee;

-- Find the minimum age of employee in the employee table
SELECT MIN(age) AS minimum_age FROM employee;

-- Find the maximum salary of employee in the employee table
SELECT MAX(salary) AS maximum_salary FROM employee;

Implement Aggregate Functions and Visually Manage PostgreSQL using DbSchema

DbSchema is a PostgreSQL client and visual designer. DbSchema has a free Community Edition, which can be downloaded here.

Implement Aggregate Functions

• Start the application and connect to the Postgres database.
• Navigate to SQL Editor section and build your query.
• Include the desired aggregate function in your query.
• Execute the query.

Conclusion

_Aggregate functions_ play a crucial role in data analysis and reporting tasks. They allow you to derive valuable insights from your database by summarizing and transforming data. In this article, we explored the concept of aggregate functions, their advantages, limitations, and how to use them in both psql and DbSchema. By mastering these functions, you can enhance your ability to extract meaningful information from your PostgreSQL database.

References

1. PostgreSQL Official Documentation: https://www.postgresql.org/docs/
2. DbSchema Documentation: https://www.dbschema.com/documentation/
3. SQL Syntax: https://www.w3schools.com/sql/
4. PostgreSQL Tutorial: https://www.postgresqltutorial.com/
5. DbSchema Interactive Diagrams: https://www.dbschema.com

Remember, the official documentation for both PostgreSQL and DbSchema is the most reliable source for up-to-date information. These resources can provide more in-depth knowledge and cover other complex aspects of creating and managing databases.