Preamble
Below is a list of data types available in PostgreSQL, which includes string, numeric, and date/time type.
String data types
Below are String data types in PostgreSQL :
|
Syntax of data types
|
Explanation
|
|---|---|
|
char (size)
|
Where size is the number of characters to store. A string of fixed lengths. Space is added to the right to the size of the characters.
|
|
character (size)
|
Where size is the number of characters to store. A string of fixed lengths. Space is added to the right to the size of the characters.
|
|
var symbol (size)
|
Where size is the number of characters to store. A string of variable lengths.
|
|
character varying(size)
|
Where size is the number of characters to store. A string of variable lengths.
|
|
text
|
The string of variable length.
|
Numerical data types
Below are the numeric data types in PostgreSQL:
|
Syntax of data types
|
Explanation
|
|---|---|
|
bit(size)
|
Bit string of fixed length,
where size is the length of a string of bits. |
|
varbit(size) bit varying(size)
|
Bit string of variable length,
where size is the length of a string of bits. |
|
smallint
|
Equivalent to int2.
2-byte integer with a sign. |
|
int
|
Equivalent to int4.
4-byte integer with a sign. |
|
integer
|
Equivalent to int4.
4-byte integer with a sign. |
|
bigint
|
A large integer value, equivalent to int8.
An 8-byte integer with a sign. |
|
smallserial
|
A small integer value with auto-increment equivalent to serial2.
2-byte integer with a sign, autoincrement. |
|
serial
|
Auto-incremental integer value, equivalent to serial4.
4-byte integer with a sign, auto-incremental. |
|
bigserial
|
Large auto-incremental integer value equivalent to serial8.
8-byte integer with a sign, auto-incremental. |
|
numeric(m,d)
|
Where m is the total number of digits, and d is the number after the decimal fraction.
|
|
double precision
|
8 bytes, double-precision, floating-point number.
|
|
real
|
4-byte floating-point single-precision number.
|
|
money
|
Cost of currency.
|
|
bool
|
Logical logical data type – true or false.
|
|
boolean
|
Logical logical data type – true or false.
|
Date/Time Types of data
Below is the date/time of the data types in PostgreSQL:
|
Syntax of data types
|
Explanation
|
|---|---|
|
date
|
Displayed as “YYYY-MM-DD”.
|
|
timestamp
|
Displayed as «YYYY-MM-DD HH:MM:SS».
|
|
timestamp without time zone
|
Displayed as «YYYY-MM-DD HH:MM:SS».
|
|
timestamp with time zone
|
Displayed as «YYYY-MM-DD HH:MM:SS-TZ».
Equivalent to the timestamptz. |
|
time
|
Displayed as «HH:MM:SS» without a time zone.
|
|
time without time zone
|
Displayed as «HH:MM:SS» without a time zone.
|
|
time with time zone
|
Displayed as «HH:MM:SS-TZ» with the time zone.
Equivalent to the time zone. |
Understanding Advanced Datatypes in PostgreSQL
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 to Improve Retail Profitability with Enteros Cost Attribution, Database Analytics, and Software Management
- 21 June 2026
- Software Engineering
Introduction The retail industry is evolving at an unprecedented pace. Driven by ecommerce growth, omnichannel experiences, customer personalization, and digital transformation initiatives, retailers are investing heavily in technology to remain competitive. Modern retail organizations rely on: Ecommerce platforms Point-of-sale (POS) systems Inventory management applications Customer engagement platforms Supply chain systems Retail analytics environments AI-powered recommendation … Continue reading “How to Improve Retail Profitability with Enteros Cost Attribution, Database Analytics, and Software Management”
How to Drive Education Platform Growth with Enteros Database Performance Analytics and RevOps Efficiency
Introduction The education sector is experiencing a profound digital transformation. Universities, colleges, online learning providers, K-12 institutions, and corporate learning platforms are increasingly relying on technology to deliver engaging learning experiences, manage academic operations, and support student success. Modern education ecosystems now support: Learning Management Systems (LMS) Student Information Systems (SIS) Online learning platforms Virtual … Continue reading “How to Drive Education Platform Growth with Enteros Database Performance Analytics and RevOps Efficiency”
Why Intelligent Database Workload Management Is Essential for High-Growth SaaS Platforms
- 19 June 2026
- Database Performance Management
Introduction Telecommunications providers are operating in one of the most competitive and technology-intensive industries in the world. While demand for connectivity, mobile services, broadband access, and digital experiences continues to grow, profit margins are increasingly challenged by rising infrastructure costs, complex network operations, and expanding customer expectations. Modern telecom organizations must support: 5G networks Cloud-native … Continue reading “Why Intelligent Database Workload Management Is Essential for High-Growth SaaS Platforms”
Reducing Operational Complexity with AI-Driven Database Observability and AIOps
Modern enterprises operate in increasingly complex digital environments. Applications now span hybrid cloud infrastructures, multi-cloud deployments, containerized platforms, microservices architectures, and globally distributed data systems. While this transformation enables greater scalability, agility, and innovation, it also creates significant operational challenges for IT and engineering teams. At the heart of these complex environments lies the database … Continue reading “Reducing Operational Complexity with AI-Driven Database Observability and AIOps”