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
Driving Telecom Efficiency with Enteros Database Software, AIOps Platform, and Cost Estimation Intelligence
- 17 March 2026
- Database Performance Management
Introduction The telecommunications industry is at the center of global digital transformation. From enabling 5G connectivity and IoT ecosystems to supporting streaming platforms, cloud applications, and enterprise communications, telecom operators are responsible for managing vast, complex, and high-performance digital infrastructures. Behind every call, message, and data transaction lies a sophisticated network of databases, applications, and … Continue reading “Driving Telecom Efficiency with Enteros Database Software, AIOps Platform, and Cost Estimation Intelligence”
Modernizing Fashion Technology with Enteros Database Management, AIOps Platform, and Cloud FinOps
Introduction The fashion industry is no longer just about creativity, design, and seasonal trends—it is now deeply rooted in data, digital platforms, and technology-driven operations. From global e-commerce platforms and mobile shopping apps to supply chain systems, inventory management tools, and customer engagement platforms, fashion enterprises rely heavily on modern IT ecosystems to drive growth … Continue reading “Modernizing Fashion Technology with Enteros Database Management, AIOps Platform, and Cloud FinOps”
Cost Transparency for Finance: How Enteros Database Software Powers Cost Attribution and RevOps Efficiency
- 16 March 2026
- Database Performance Management
Introduction Financial institutions today operate in a highly data-driven and technology-intensive environment. From digital banking platforms and trading systems to fraud detection engines and regulatory reporting tools, modern financial operations depend on complex data infrastructures that process enormous volumes of transactions every second. As financial organizations continue to modernize their technology stacks—moving to cloud environments, … Continue reading “Cost Transparency for Finance: How Enteros Database Software Powers Cost Attribution and RevOps Efficiency”
Cloud FinOps for Banking: How Enteros Database Management Platform Enables Intelligent Cost and Performance Control
Introduction The banking industry is experiencing one of the most significant technological transformations in its history. Digital banking platforms, real-time payments, mobile applications, open banking APIs, fraud detection systems, and AI-driven financial services are reshaping how financial institutions operate and deliver value to customers. Behind these digital services lies a complex technology infrastructure powered by … Continue reading “Cloud FinOps for Banking: How Enteros Database Management Platform Enables Intelligent Cost and Performance Control”