Preamble
PostgreSQL MAX function returns the maximum value of the expression.
Syntax of the max function in PostgreSQL
SELECT max(aggregate_expression_id)
FROM tabs
[WHERE conds];
Or the syntax of the max function when grouping results into one or more columns:
SELECT expression1_id,2_id,..._n_id,
max(aggregate_expression_id)
FROM tabs
[WHERE conds]
GROUP BY expression1_id,2_id,..._n_id;
Parameters and arguments of the function
- expression1_id, expression2_id,… expression_n_id – Expressions that are not enclosed in the max function and must be included in the GROUP BY operator at the end of the SQL query.
- aggregate_expression_id – This is the column or expression from which the maximum value will be returned.
- tabs – 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 max 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
Let’s look at some examples of max functions to understand how to use max in PostgreSQL.
For example, you might want to select all entries with a maximum quantity from an inventory table as max(quantity) AS “Highest Quantity”.
SELECT max(quantity) AS "Highest Quantity"
FROM inventory;
In this example of the max function, we added the expression max(quantity) AS “Highest Quantity”. As a result, “Highest Quantity” will be displayed as the field name when the result set is returned.
Example using GROUP BY
In some cases, you will need to use the GROUP BY operator with the max function.
For example, you can also use the max function to return a department and the maximum quantity from the inventory table.
SELECT depart, max(quantity) AS "Highest Quantity"
FROM inventory
GROUP BY depart;
Since your SELECT operator has one column that is not encapsulated in the max function, you should use the GROUP BY operator. That’s why the department field shall be specified in the GROUP BY operator.
PostgreSQL: Calculating Min, Max & Average
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
How Intelligent Database Analytics Improves Data Infrastructure Performance in Modern Hospital Management Systems
- 20 May 2026
- Database Performance Management
Introduction Healthcare organizations are undergoing rapid digital transformation. Hospitals now rely on advanced technologies such as Electronic Health Records (EHR), telemedicine platforms, AI-driven diagnostics, medical imaging systems, and hospital management systems to deliver faster and more efficient patient care. At the center of these systems lies a powerful data infrastructure supported by complex databases. Modern … Continue reading “How Intelligent Database Analytics Improves Data Infrastructure Performance in Modern Hospital Management Systems”
Enhancing Retail Platform Performance and Customer Experience with Intelligent Database Analytics
- 19 May 2026
- Database Performance Management
Retail has undergone a major transformation over the past decade. With the rapid growth of digital commerce, omnichannel experiences, and real-time customer engagement, modern retail platforms depend heavily on high-performance data infrastructure. From product searches and inventory updates to personalized recommendations and checkout transactions, every step of the customer journey relies on fast, reliable, and … Continue reading “Enhancing Retail Platform Performance and Customer Experience with Intelligent Database Analytics”
How to Optimize Retail Infrastructure Costs with Enteros Database Optimization and Azure Cost Allocation
Introduction The retail industry is rapidly evolving as organizations embrace cloud computing, AI-driven analytics, ecommerce expansion, omnichannel operations, and data-intensive customer engagement platforms. Modern retailers must manage increasingly complex digital infrastructures while delivering seamless customer experiences across online stores, physical retail locations, mobile applications, and supply chain ecosystems. Today’s retail organizations rely heavily on digital … Continue reading “How to Optimize Retail Infrastructure Costs with Enteros Database Optimization and Azure Cost Allocation”
How to Strengthen Ecommerce Scalability with Enteros AIOps Platform and Financial Intelligence
Introduction The ecommerce industry is evolving rapidly as organizations embrace cloud-native infrastructures, AI-powered customer engagement, real-time analytics, and digital commerce platforms to support global growth and customer expectations. Modern ecommerce businesses must manage massive operational complexity while delivering seamless digital experiences across websites, mobile applications, marketplaces, and omnichannel retail systems. Today’s ecommerce organizations rely heavily … Continue reading “How to Strengthen Ecommerce Scalability with Enteros AIOps Platform and Financial Intelligence”