Preamble
Oracle/PLSQL LNNVL function is used in the WHERE SQL query sentence to evaluate the state when one of the operands may contain the value NULL.
Oracle/PLSQL syntax of LNNVL function
LNNVL( condition_id )
The LNNVL function will return to the following:
| The condition is assessed as | LNNVL will return the value |
| TRUE | FALSE |
| FALSE | TRUE |
| UNKNOWN | TRUE |
So, if we had two columns called qty and reorder_level, where qty = 20 and reorder_level IS NULL, the function LNNVL would return the following:
| Condition | The condition is assessed as | LNNVL will return the value |
| qty = reorder_level | UNKNOWN | TRUE |
| qty IS NULL | FALSE | TRUE |
| reorder_level IS NULL | TRUE | FALSE |
| qty = 20 | TRUE | FALSE |
| reorder_level = 20 | UNKNOWN | TRUE |
LNNVL function in the following versions of Oracle/PLSQL
Oracle 12c, Oracle 11g, Oracle 10g
The LNNVL function can be used in Oracle PLSQL.
Let’s have a look at an example. If we had a product table containing the following data:
| PROD_ID | QTY_ID | REORDER_LEVEL_ID |
| 1000 | 20 | NULL |
| 2000 | 15 | 8 |
| 3000 | 8 | 10 |
| 4000 | 12 | 6 |
| 5000 | 2 | 2 |
| 6000 | 4 | 5 |
And we wanted to find all the products whose QTY was below REORDER_LEVEL, let’s run the next SQL query:
SELECT *
FROM prods
WHERE QTY < REORDER_LEVEL;
The request will return the following result:
| PROD_ID | QTY_ID | REORDER_LEVEL_ID |
| 3000 | 8 | 10 |
| 6000 | 4 | 5 |
However, if we wanted to consider products that were lower than REORDER_LEVEL and REORDER_LEVEL had the value NULL, we would use the function LNNVL as follows:
SELECT *
FROM prods
WHERE LNNVL(QTY >= REORDER_LEVEL);
This will return the next result:
| PROD_ID | QTY_ID | REORDER_LEVEL_ID |
| 1000 | 20 | NULL |
| 3000 | 8 | 10 |
| 6000 | 4 | 5 |
In this example, the resulting set also contains prod_id 1000, which has REORDER_LEVEL NULL.
LNNVL FUNCTION IN ORACLE SQL
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 to Optimize Insurance Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability
- 11 August 2026
- Database Performance Management
Introduction Insurance companies manage enormous volumes of policy, claims, underwriting, customer, actuarial, billing, and regulatory data. Their technology environments must support millions of transactions while delivering fast digital experiences to policyholders, agents, brokers, and employees. Database performance directly influences claims processing, policy administration, underwriting, customer service, billing, and reporting. Enteros helps insurance organizations optimize these … Continue reading “How to Optimize Insurance Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability”
How to Optimize Banking Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability
Introduction Banking organizations depend on databases for virtually every critical transaction. Account management, payments, lending, fraud detection, online banking, credit processing, wealth management, and regulatory reporting all require reliable and high-performing database infrastructure. As financial institutions modernize through cloud computing, AI, digital banking, open banking, and real-time payments, database environments are becoming increasingly complex. Enteros … Continue reading “How to Optimize Banking Operations with Enteros Database Software, AI-Powered Analytics, and Database Observability”
How AIOps and FinOps Improve Cloud Performance and Cost Management in BFSI
Introduction The Banking, Financial Services, and Insurance (BFSI) industry is undergoing rapid digital transformation. Mobile banking, digital payments, online lending, insurance platforms, wealth management applications, fraud detection systems, and real-time financial services increasingly depend on cloud infrastructure to deliver fast, reliable, and scalable experiences. However, cloud adoption also introduces new operational and financial challenges. BFSI … Continue reading “How AIOps and FinOps Improve Cloud Performance and Cost Management in BFSI”
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”