Preamble
The PostgreSQL UPDATE statement is used to update existing table entries in a PostgreSQL database.
The syntax for the UPDATE statement when updating a single table in PostgreSQL
UPDATE table
SET column1 = expression1_id | DEFAULT,
column2 = expression2_id | DEFAULT,
…
[WHERE conds];
Parameters and arguments of the statement
- column1, column2 – Columns that you want to update.
- expression1_id, column2_id – New values for assigning column1, column2. Therefore, column1 will be assigned the value expression1, column2 will be assigned the value2, etc.
- DEFAULT – The default value for this particular column in the table. If the default value for a column is not set, the column will be set to NULL.
- WHERE conds – Optional. The conditions that must be met to perform the update. If no conditions are set, all entries in the table will be updated.
Example of how to update a single column
Let’s look at a very simple example of a PostgreSQL UPDATE query.
UPDATE contacts
SET first_name = 'Helen'
WHERE contact_id = 35;
In this example, the value of first_name will be updated to ‘Helen’ in the contacts table, where contact_id is 35.
You can also use the keyword DEFAULT to set the default value for the column.
For example,
UPDATE contacts
SET first_name = DEFAULT
WHERE contact_id = 35;
In this example, the first_name will be updated to the default value for the field in the contacts table, where contact_id is 35. If the default value is not present in the contacts table, the first_name column will be set to NULL.
Example how to update several columns
Consider the UPDATE example for PostgreSQL, where you can update several columns with one UPDATE statement.
UPDATE contacts
SET city = 'Abilene',
state = 'Beaumont'
WHERE contact_id >= 200;
If you want to update multiple columns, you can do so by separating the column/value pairs with commas.
In this PostgreSQL example of UPDATE, the value of the city will be changed to ‘Abilene’ and the state will be changed to ‘Beaumont’ where contact_id is greater than or equal to 200.
PostgreSQL: How to Update Records | Course
About Enteros
IT organizations routinely spend days and weeks troubleshooting production database performance issues across multitudes of critical business systems. Fast and reliable resolution of database performance problems by Enteros enables businesses to generate and save millions of direct revenue, minimize waste of employees’ productivity, reduce the number of licenses, servers, and cloud resources and maximize the productivity of the application, database, and IT operations teams.
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 Can Banks Improve Database Performance for Real-Time Payment Processing?
- 7 September 2026
- Database Performance Management
Strong bank database performance is essential for real-time payment processing because every payment depends on databases retrieving, validating, updating, and recording financial information with minimal delay. Banks can improve bank database performance by optimizing high-impact SQL queries, reducing locking and resource contention, monitoring transaction latency, establishing workload baselines, planning capacity, and using database observability to … Continue reading “How Can Banks Improve Database Performance for Real-Time Payment Processing?”
How Can Database Observability Help Banks Prevent Transaction Failures and Downtime?
With database observability, banks can identify performance anomalies, see how databases are performing, find bottlenecks, and troubleshoot the root causes of transaction failures before they turn into major service outages. Database observability provides banking IT teams with increased visibility into queries, workloads, resource consumption, waits, blocking and performance patterns as they evolve. This proactive visibility … Continue reading “How Can Database Observability Help Banks Prevent Transaction Failures and Downtime?”
How Can Database Observability Help Retail Companies Resolve Performance Issues Faster?
Database observability for retail helps retailers detect, understand, and resolve performance problems faster by providing deep visibility into database workloads, queries, waits, anomalies, and dependencies. Instead of reacting only after applications slow down, retail IT teams can identify root causes earlier, reduce troubleshooting time, improve uptime, and strengthen database performance monitoring for retail across stores, … Continue reading “How Can Database Observability Help Retail Companies Resolve Performance Issues Faster?”
How Can Database Observability Help Telecom Companies Resolve Performance Issues Faster?
Database observability for telecom helps telecom companies detect, investigate, and resolve database performance problems faster by providing deeper visibility into queries, workloads, waits, dependencies, and anomalies. Instead of relying only on basic infrastructure metrics, teams can identify root causes sooner, reduce troubleshooting time, improve service reliability, and support stronger telecom database performance across complex, high-volume … Continue reading “How Can Database Observability Help Telecom Companies Resolve Performance Issues Faster?”