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
How Can Banks Prevent Database Bottlenecks in Real-Time Financial Transactions?
- 1 September 2026
- Database Performance Management
Banks can prevent database bottlenecks in banking by continuously monitoring SQL workloads, optimizing queries and indexes, detecting resource contention, analyzing transaction latency, and identifying anomalies before performance declines. Effective database performance monitoring for banks provides real-time workload visibility and faster root cause analysis. Enteros helps financial institutions identify database inefficiencies and maintain reliable, scalable transaction … Continue reading “How Can Banks Prevent Database Bottlenecks in Real-Time Financial Transactions?”
Why Is Database Observability Important for Modern Hospitals and Healthcare IT Teams?
Database observability in healthcare is important because hospitals depend on databases to power EHRs, patient portals, clinical applications, billing, analytics, and other essential systems. It gives healthcare IT teams deeper visibility into SQL workloads, resource usage, bottlenecks, and performance changes. Enteros supports this approach with database observability, AI-powered analysis, anomaly detection, and root cause analysis … Continue reading “Why Is Database Observability Important for Modern Hospitals and Healthcare IT Teams?”
How Can Healthcare Organizations Reduce Cloud Database Costs With AIOps and FinOps
Healthcare organizations can reduce cloud database costs by combining AIOps with FinOps to identify inefficient workloads, detect anomalies, rightsize resources, and connect database performance with infrastructure spending. Effective healthcare cloud database cost optimization focuses on eliminating waste without compromising reliability. Enteros supports this approach through database observability, AI-powered SQL analysis, root cause analysis, performance intelligence, … Continue reading “How Can Healthcare Organizations Reduce Cloud Database Costs With AIOps and FinOps”
What Database Performance Challenges Do Hospitals Face in Cloud-Based Healthcare Systems?
Hospitals face database performance challenges such as slow SQL queries, growing healthcare data, EHR performance issues, database bottlenecks, resource contention, cloud complexity, and rising costs. Cloud-based healthcare systems require continuous database monitoring, workload optimization, anomaly detection, and root-cause analysis. AI-powered AIOps can help hospitals improve database performance, reliability, scalability, and cloud cost efficiency while supporting … Continue reading “What Database Performance Challenges Do Hospitals Face in Cloud-Based Healthcare Systems?”