Preamble
In Oracle PL/SQL Varray (an array with variable size) is an array whose number of elements can vary from zero (empty) to the declared maximum size.
To access a Varray element, use the variable_name(index) syntax:
- The lower boundary of the index is 1; the upper boundary is the current number of elements.
- The upper limit changes when the elements are added or removed, but it cannot exceed the maximum size.
When you store and extract a varray from the database, its indexes and the order of the elements remain stable.
Syntax to define and then declare a Varrays type variable in Oracle PL/SQL
TYPE type_varray IS {VARRAY | VARYING ARRAY} (size_limit) OF element_type [NOT NULL];
v_arr type_varray;
Parameters and arguments of the array
- type_varray – name of type Varray
- element_type – any PL/SQL data type, except for REF CURSOR
- size_limit is a positive integer literal representing the maximum number of elements in the array.
- v_arr – the name of a variable of the Varray type
Note:
- When defining a Varray type, you must specify its maximum size.
An example of how to use Varray in Oracle PL/SQL
DECLARE
TYPE Foursome IS VARRAY(4) OF VARCHAR2(15); -- Varray type
-- is a varray variable initialized by the constructor:
team Foursome := Foursome('John', 'Mary', 'Alberto', 'Juanita');
PROCEDURE print_team (heading VARCHAR2) IS
BEGIN
DBMS_OUTPUT.PUT_LINE(heading);
FOR i IN 1..4 LOOP
DBMS_OUTPUT.PUT_LINE(i) || '.' || team(i));
END LOOP;
DBMS_OUTPUT.PUT_LINE('---');
END;
BEGIN
print_team('2001 Team:');
team(3) := 'Pierre'; -- Change the values of the two elements
team(4) := 'Yvonne';
print_team('2005 Team:');
-- Call the constructor to assign new values to the Varray variable:
team := Foursome('Arun', 'Amitha', 'Allan', 'Mae');
print_team('2009 Team:');
END;
As a result, we get:
2001 Team:
1.John
2.Mary
3.Alberto
4.Juanita
---
2005 Team:
1.John
2.Mary
3.Pierre
4.Yvonne
---
2009 Team:
1.Arun
2.Amitha
3.Allan
4.Mae
---
In this example we defined Foursome as a local Varray type, declared a team variable of this type (initialized by the constructor) and defined the print_team procedure which printed Varray. The example calls the procedure three times:
- after initializing the variable,
- after changing values of two elements separately,
- and after using the constructor to change the value of all elements.
Using Varray
Varray should be used when:
- You know the maximum number of elements.
- You access the elements in sequence.
- Since you must store or retrieve all elements simultaneously, Varray may not be practical for a large number of elements.
PL/SQL tutorial: VARRAYs in Oracle Database
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
Scaling Digital Retail Seamlessly: Enteros’ AI-Driven Blueprint for Cost-Efficient eCommerce Performance
- 30 November 2025
- Database Performance Management
Introduction The global eCommerce landscape is expanding at a rapid pace, fueled by digital-native consumers, demand for instant gratification, multi-channel retailing, and hyper-personalized buying journeys. As retailers scale across cloud platforms, microservices, distributed databases, and SaaS ecosystems, IT complexity grows exponentially.This technological acceleration creates enormous pressure on performance management, cost estimation, and customer experience delivery. … Continue reading “Scaling Digital Retail Seamlessly: Enteros’ AI-Driven Blueprint for Cost-Efficient eCommerce Performance”
Future-Ready Retail IT: How Enteros Unifies Cloud Resource Governance and GenAI Cost Attribution
Introduction The modern retail landscape is evolving at unprecedented speed. From omnichannel commerce to hyper-personalized recommendations, digital storefronts, automated supply chains, and AI-driven merchandising, retailers are racing to deliver fast, reliable, and data-rich experiences.But this acceleration has created new layers of complexity—especially across cloud infrastructure, distributed databases, microservices, SaaS integrations, and analytics engines. These environments … Continue reading “Future-Ready Retail IT: How Enteros Unifies Cloud Resource Governance and GenAI Cost Attribution”
Fashion’s Digital Future: How Enteros Enhances Cost Attribution and Data Intelligence with GenAI and AI SQL
- 27 November 2025
- Database Performance Management
Introduction The global fashion industry is undergoing one of the biggest transformations in its history. As brands expand across digital channels, adopt omnichannel retail models, and operate high-volume supply chains, their IT ecosystems are becoming more complex than ever. Massive data streams flow through ERP systems, PLM platforms, inventory management solutions, eCommerce engines, POS networks, … Continue reading “Fashion’s Digital Future: How Enteros Enhances Cost Attribution and Data Intelligence with GenAI and AI SQL”
Smarter Retail Ops: How Enteros Unifies Resource Group Management, Cloud FinOps, and RevOps Efficiency
Introduction The retail sector is undergoing a profound digital reinvention, driven by the rapid expansion of omnichannel commerce, real-time inventory systems, dynamic pricing engines, and data-intensive personalization platforms. As retail enterprises scale across cloud environments, their IT complexity grows, introducing massive volumes of infrastructure resources, distributed databases, and SaaS ecosystems. In this fast-moving environment, resource … Continue reading “Smarter Retail Ops: How Enteros Unifies Resource Group Management, Cloud FinOps, and RevOps Efficiency”