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 AIOps and FinOps Drive Cost-Efficient Digital Transformation in Banking
- 9 August 2026
- Database Performance Management
Introduction Digital transformation has become a strategic priority for banks and financial institutions. From mobile banking and digital payments to real-time fraud detection, personalized financial services, and cloud-native applications, banking organizations are investing heavily in technology to deliver faster, smarter, and more convenient customer experiences. However, digital transformation also introduces a major challenge: how can … Continue reading “How AIOps and FinOps Drive Cost-Efficient Digital Transformation in Banking”
How to Optimize Healthcare Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability
Introduction Healthcare organizations operate some of the most data-intensive technology environments in the world. Hospitals, health systems, insurers, pharmaceutical organizations, laboratories, and digital health companies depend on databases to manage electronic health records, patient scheduling, billing, clinical workflows, medical imaging, pharmacy systems, and operational analytics. Database performance directly affects clinician productivity, patient access, administrative efficiency, … Continue reading “How to Optimize Healthcare Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability”
How to Optimize Telecommunications Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability
Introduction The telecommunications industry is undergoing rapid transformation as providers expand 5G networks, fiber infrastructure, cloud services, IoT platforms, edge computing, and AI-driven customer experiences. Telecom operators must process enormous volumes of network, subscriber, billing, usage, device, and customer data while delivering highly reliable services around the clock. Every subscriber authentication, call record, billing transaction, … Continue reading “How to Optimize Telecommunications Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability”
How Intelligent Database Observability Helps Enterprises Achieve Operational Excellence
- 7 August 2026
- Database Performance Management
Introduction In today’s digital-first economy, operational excellence is no longer just a business objective—it is a competitive necessity. Whether organizations operate in banking, healthcare, retail, manufacturing, telecommunications, or SaaS, customers expect applications to be available 24/7, transactions to process instantly, and digital experiences to remain seamless. At the center of these business-critical applications lies one … Continue reading “How Intelligent Database Observability Helps Enterprises Achieve Operational Excellence”