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 SaaS Companies Detect Database Performance Issues Across Multi-Tenant Environments?
- 18 September 2026
- Database Performance Management
SaaS companies can detect database performance problems across multi-tenant environments by continuously monitoring tenant workloads, SQL activity, resource consumption, query latency, database waits, locking, transaction volume, and unusual workload patterns. Effective SaaS database performance monitoring helps teams identify resource-heavy tenants, detect emerging bottlenecks, investigate inefficient SQL, and maintain consistent application performance as customer activity grows. … Continue reading “How Can SaaS Companies Detect Database Performance Issues Across Multi-Tenant Environments?”
How Can Airlines Prevent Database Bottlenecks During Peak Booking and Check-In Periods?
Airlines can prevent database bottlenecks during peak booking and check-in periods by continuously monitoring SQL workloads, database latency, resource utilization, wait events, locks, transaction volumes, and unusual workload changes. Effective airline database performance monitoring helps IT teams detect emerging problems early, identify their root causes, optimize inefficient queries, and prepare database capacity before passenger demand … Continue reading “How Can Airlines Prevent Database Bottlenecks During Peak Booking and Check-In Periods?”
How Can Telecom IT Teams Monitor Database Performance Across Complex Network Environments?
- 17 September 2026
- AIDatabase Performance Management
Telecom IT teams can monitor complex database environments by combining real-time observability, SQL analysis, workload baselines, anomaly detection, capacity monitoring, and root cause analysis. Effective telecom database performance monitoring gives teams centralized visibility across distributed systems. With strong database performance management, Enteros helps identify bottlenecks, analyze workloads, improve reliability, and detect emerging performance risks before … Continue reading “How Can Telecom IT Teams Monitor Database Performance Across Complex Network Environments?”
How Can Retail IT Teams Prevent Database Problems From Affecting Online Orders?
Retail IT teams can protect online orders by continuously monitoring SQL queries, latency, waits, locks, transactions, and infrastructure health. Strong Retail Database Performance practices help detect bottlenecks before they disrupt carts, checkout, inventory, or payments. AI database monitoring for retail adds anomaly detection and predictive insights, while Enteros helps teams diagnose issues faster and maintain … Continue reading “How Can Retail IT Teams Prevent Database Problems From Affecting Online Orders?”