Preamble
The PostgreSQL avg function returns the average value of the expression.
Syntax of avg function in PostgreSQL
SELECT avg(aggregate_expression)
FROM tables
[WHERE conditions];
Or the syntax of the avg function when grouping results into one or more columns:
SELECT expression1,2,..._n,
avg(aggregate_expression)
FROM tables
[WHERE conditions]
GROUP BY expression1,2,..._n;
Parameters and arguments of the function
- expression1, expression2,… expression_n – Expressions that are not enclosed in the function avg and must be included in the GROUP BY operator at the end of the SQL query.
- expression_expression – This is a column or expression to be averaged.
- tables – These are the tables from which you want to get the records. At least one table must be specified in the FROM operator.
- WHERE conditions – Optional. These are the conditions that must be met to select records.
The avg function can be used in the following PostgreSQL versions
PostgreSQL 11, PostgreSQL 10, PostgreSQL 9.6, PostgreSQL 9.5, PostgreSQL 9.4, PostgreSQL 9.3, PostgreSQL 9.2, PostgreSQL 9.1, PostgreSQL 9.0, PostgreSQL 8.4.
Single Expression Example
Consider some examples of avg functions to understand how to use avg in PostgreSQL.
For example, you might be wondering what the average number of fields in an inventory table is.
SELECT avg(quantity) AS "Average Quantity"
FROM inventory;
In this example of the avg function, we used the alias avg(quantity) as “Average Quantity”. As a result, “Average Quantity” will appear as the field name when the result set returns.
Example using DISTINCT
You can use the DISTINCT operator in the avg function. For example, the SQL statement below returns the average of a unique quantity, where product_type is ‘Hardware’.
SELECT avg(DISTINCT quantity) AS "Average Quantity"
FROM inventory
WHERE product_type = 'Hardware';
If there were two quantities equal to 25, only one of these values would be used in the avg function.
Using formula
The expression contained in the avg function does not necessarily have to be a single field. You can also use a formula. For example, you may want to use the “Average Commission”.
SELECT avg(sales * 0.10) AS "Average Commission"
FROM orders;
Example using GROUP BY
You can also use the avg function to return the department and “Average Quantity” (the average number in the corresponding department). For example,
SELECT department, avg(quantity) AS "Average Quantity"
FROM inventory
GROUP BY department;
Since your SELECT operator has one column that is not encapsulated in the avg function, you should use the GROUP BY operator. Therefore, the department field should be specified in the GROUP BY section.
About Enteros
Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of clouds, RDBMS, NoSQL, and machine learning database platforms.
The views expressed on this blog are those of the author and do not necessarily reflect the opinions of Enteros Inc. This blog may contain links to the content of third-party sites. By providing such links, Enteros Inc. does not adopt, guarantee, approve, or endorse the information, views, or products available on such sites.
Are you interested in writing for Enteros’ Blog? Please send us a pitch!
RELATED POSTS
Why BFSI Leaders Are Turning to Enteros for Database Optimization, AI Ops, and Cloud FinOps Excellence
- 16 April 2026
- Database Performance Management
Introduction The Banking, Financial Services, and Insurance (BFSI) sector is undergoing a massive digital transformation. With the rise of digital banking, real-time payments, fraud detection systems, and AI-driven financial services, organizations are becoming increasingly dependent on high-performance data infrastructure. From managing millions of transactions per second to enabling real-time risk analysis and personalized customer experiences, … Continue reading “Why BFSI Leaders Are Turning to Enteros for Database Optimization, AI Ops, and Cloud FinOps Excellence”
How to Optimize Telecom Sector Growth with Enteros AIOps Platform, Resource Metadata, Hierarchy Metadata, Spot Instances, and RevOps Efficiency
Introduction The telecom sector is at the center of global digital transformation, enabling connectivity for billions of users, businesses, and emerging technologies like IoT, 5G, and edge computing. As demand for high-speed, reliable communication services continues to rise, telecom providers are under immense pressure to scale operations efficiently while maintaining performance and controlling costs. However, … Continue reading “How to Optimize Telecom Sector Growth with Enteros AIOps Platform, Resource Metadata, Hierarchy Metadata, Spot Instances, and RevOps Efficiency”
Who Should Adopt Enteros for Retail Growth Management with AI SQL and Cloud FinOps Efficiency
Introduction The retail sector is evolving at an unprecedented pace, driven by digital transformation, omnichannel experiences, and data-driven decision-making. From global eCommerce giants to mid-sized retail chains, businesses are increasingly relying on cloud infrastructure, databases, and analytics platforms to fuel growth. However, this rapid expansion introduces a fundamental challenge:how to scale efficiently while maintaining performance, … Continue reading “Who Should Adopt Enteros for Retail Growth Management with AI SQL and Cloud FinOps Efficiency”
How to Optimize Technology Sector Growth with Enteros Database Management Platform, Cloud FinOps, and RevOps Efficiency
Introduction The technology sector is at the forefront of innovation, powering digital transformation across industries. From SaaS platforms and cloud-native applications to AI-driven solutions, technology companies are scaling rapidly to meet growing global demand. However, this rapid expansion introduces a critical challenge:how to sustain growth while maintaining high-performance systems, controlling cloud costs, and aligning operations … Continue reading “How to Optimize Technology Sector Growth with Enteros Database Management Platform, Cloud FinOps, and RevOps Efficiency”