Preamble
Below is a list of data types available in Oracle/PLSQL, which includes character, numeric, date/time, Boolean LOB, RowId data types.
Symbolic data types
Below are the character data types in Oracle/PLSQL:
| Data Types | Size | Description |
| char(size) | Maximum size is 2000 bytes. | Where the size is the number of characters of a fixed length. If the stored value is shorter, it is supplemented with spaces; if it is longer, an error is generated. |
| nchar(size) | Maximum size is 2000 bytes. | Where Size – the number of characters of fixed length in Unicode encoding. If the stored value is shorter, it is supplemented with spaces; if it is longer, an error is generated. |
| nvarchar2(size) | Maximum size is 4000 bytes. | Where Size – number of saved characters in Unicode encoding of variable length. |
| varchar2(size) | Maximum size is 4000 bytes. Maximum size in PLSQL is 32KB. |
Where Size – number of saved characters of variable length. |
| long | The maximum size is 2GB. | Symbolic data of variable length. |
| raw | Maximum size is 2000 bytes. | Contains binary data of variable length. |
| long raw | The maximum size is 2GB. | Contains binary data of variable length. |
Application: Oracle 9i, Oracle 10g, Oracle 11g, Oracle 12c
Numerical data types
Below are the numeric data types in Oracle/PLSQL:
| Data Types | Size | Description |
| number(accuracy,scale) | The accuracy can be in the range of 1 to 38. The scale can be in the range of -84 to 127. |
For example, number (14.5) is a number that has 9 decimal places and 5 decimal places.
|
| numeric(accuracy,scale) | The accuracy can be in the range of 1 to 38. |
For example, numeric(14,5) is a number that has 9 decimal places and 5 decimal places.
|
| dec(accuracy,scale) | The accuracy can be in the range of 1 to 38. |
For example, dec (5,2) is a number that has 3 digits before the decimal point and 2 digits after.
|
| decimal(accuracy,scale) | The accuracy can be in the range of 1 to 38. |
For example, decimal (5,2) is a number that has 3 digits before the decimal point and 2 digits after.
|
| PLS_INTEGER | Integer numbers ranging from -2,147,483,648 to 2,147,483,647 |
PLS_INTEGER value requires less memory and faster NUMBER values.
|
| Maximum size is 2000 bytes. | Contains binary data of variable length. | |
| long raw | The maximum size is 2GB. | Contains binary data of variable length. |
Application: Oracle 9i, Oracle 10g, Oracle 11g, Oracle 12c
Date/time data types
Below are the date/time data types in Oracle/PLSQL:
| Data Types | Size |
| date | The date may take values from 1 January 4712 BC to 31 December 9999 AD. |
Application: Oracle 9i, Oracle 10g, Oracle 11g, Oracle 12c
Large objects (LOB) data types
The LOB data types in Oracle/PLSQL are listed below:
| Data Types | Size | Description |
| bfile | Maximum file size 4 GB. |
File locators, points to the binary file in the server file system (outside the database).
|
| blob | Stores up to 4 GB of binary data. | Stores unstructured binary large objects. |
| clob | Stores up to 4 GB of character data. | Stores single-byte and multi-byte character data. |
| nclob | Stores up to 4 GB of character text data. | Saves data in unicode encoding. |
Application: Oracle 9i, Oracle 10g, Oracle 11g, Oracle 12c
Rowid data type
The Rowid data types in Oracle/PLSQL are listed below:
| Data Types | Format | Description |
| rowid | The format of the line:BBBBBB.RRRR.FFFFF, Where BBBBB is a block in a database file; RRRR is a string in a block; FFFFF is a database file. |
Fixed-length binary data. Each record in the database has a physical address or rowid.
|
Boolean (BOOLEAN) data types
| Data Types | Format | Description |
| BOOLEAN | TRUE or FALSE. Can take value NULL |
Stores logical values that you can use in logical operations.
|
Application: Oracle 9i, Oracle 10g, Oracle 11g, Oracle 12c
Oracle SQL Tutorial; Intro to Data Types
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
AI-Powered Retail Growth: How Enteros Aligns Database Performance with RevOps Efficiency
- 22 December 2025
- Database Performance Management
Introduction The retail sector is experiencing a profound digital shift. Omnichannel commerce, hyper-personalized customer experiences, dynamic pricing, real-time inventory visibility, and rapid fulfillment have become the standard rather than the exception. Behind every seamless customer interaction lies a complex technology ecosystem powered by databases, cloud infrastructure, SaaS platforms, analytics engines, and AI-driven applications. As retail … Continue reading “AI-Powered Retail Growth: How Enteros Aligns Database Performance with RevOps Efficiency”
Modernizing Banking IT: How Enteros Combines Database Performance Management, AIOps, and Cloud FinOps
Introduction The banking sector is undergoing one of the most significant technology transformations in its history. Digital banking platforms, real-time payments, mobile apps, AI-driven fraud detection, open banking APIs, and regulatory reporting systems now operate at massive scale and speed. At the heart of all these capabilities lies a complex web of databases and cloud … Continue reading “Modernizing Banking IT: How Enteros Combines Database Performance Management, AIOps, and Cloud FinOps”
Intelligent Healthcare IT Economics: How Enteros Unifies Cost Attribution, Performance Management, and Cloud FinOps with AIOps
- 21 December 2025
- Database Performance Management
Introduction Healthcare organizations today are under immense pressure to deliver better patient outcomes while managing rising operational costs, complex regulatory requirements, and rapidly expanding digital ecosystems. From electronic health records (EHRs) and clinical decision systems to telehealth platforms, AI-driven diagnostics, and revenue cycle applications, healthcare IT environments have become both mission-critical and highly complex. At … Continue reading “Intelligent Healthcare IT Economics: How Enteros Unifies Cost Attribution, Performance Management, and Cloud FinOps with AIOps”
Smart Real Estate IT Operations: How Enteros Uses AIOps to Optimize Database Performance and Cost Estimation
Introduction The real estate sector is undergoing a profound digital transformation. What was once a traditionally asset-heavy, manually operated industry is now driven by data, cloud platforms, and real-time analytics. From property management systems and leasing platforms to smart building technologies, digital twin models, and AI-powered valuation engines, modern real estate enterprises rely heavily on … Continue reading “Smart Real Estate IT Operations: How Enteros Uses AIOps to Optimize Database Performance and Cost Estimation”