Preamble
Comparison operators are used in the WHERE sentence to determine which records to select. Here is a list of comparison statements that you can use in Oracle PL/SQL:
| Comparator operators | Description |
| = | Exactly |
| Whatever | |
| More than | |
| More or equal | |
| < | Less than |
| <= | Less or equal |
| IN ( ) |
Corresponds to the value on the list
|
| NOT | Negates the condition |
| BETWEEN | Within range (inclusive) |
| IS NULL | NULL value |
| IS NOT NULL | Value, not NULL |
| LIKE | Template matching % and _ |
| REGEXP_LIKE |
Comparison of templates with regular expressions
|
| EXISTS |
This condition is fulfilled if the subquery returns at least one line
|
Consider examples of comparison operators that you can use in Oracle PL/SQL.
Example of an equality operator
In Oracle PL/SQL you can use the = operator to verify equality in a query.
For example, you can use an = operator:
SELECT *
FROM contacts
WHERE last_name = 'Bernard';
In this example, the SELECT operator returns all the rows from the contacts table where last_name equals Bernard.
Here is an example of the inequality operator <>, !=
In Oracle PL/SQL, you can use the <> or != operators. To check inequality in a query.
For example, we could check for inequality using the <> operator in the following way:
SELECT *
FROM contacts
WHERE last_name <> 'Bernard';
In this example, the SELECT operator will return all the rows from the contacts table where last_name does not equal Bernard.
Or you may also write this query using the != operator in the following way:
SELECT *
FROM contacts
WHERE last_name != 'Bernard';
Both these requests will return the same results.
An example of an operator larger than >
You can use the > operator in Oracle PL/SQL to check the expression “more than”.
SELECT *
FROM contacts
WHERE contact_id > 20;
In this example, the SELECT statement will return all rows from the contacts table where contact_id is greater than 20. Contact_id equal to 20 will not be included in the result set.
Example operator greater than or equal to >=
In Oracle PL/SQL you can use the >= operator to check the expression, “more or equal”.
SELECT *
FROM contacts
WHERE contact_id >= 20;
In this case, the contact id must be higher than or equal to 20 for the SELECT operator to retrieve all records from the contacts table. Therefore in an instance, the resultant set will have the contact id value of 20.
The example of the operator is less than <
You can use the < operator in Oracle PL/SQL to check the expression “less than”.
SELECT *
FROM contacts
WHERE contact_id < 150;
In this example, the SELECT operator will return all rows from the contacts table where contact_id is less than 150. contact_id equal to 150 will not be included in the resulting set.
The example of the operator is less than or equal to <=
In Oracle PL/SQL you can use the <= operator to check for an expression that is “less or equal”.
SELECT *
FROM contacts
WHERE contact_id <= 150;
In this case, the SELECT operator will provide a list of all contacts database rows with contact id values of 150 or less. Therefore, for instance, the 150th product id will be a part of the final set.
Comparison Operators (Introduction to 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 Can Banks Improve Database Performance for Real-Time Payment Processing?
- 7 September 2026
- Database Performance Management
Strong bank database performance is essential for real-time payment processing because every payment depends on databases retrieving, validating, updating, and recording financial information with minimal delay. Banks can improve bank database performance by optimizing high-impact SQL queries, reducing locking and resource contention, monitoring transaction latency, establishing workload baselines, planning capacity, and using database observability to … Continue reading “How Can Banks Improve Database Performance for Real-Time Payment Processing?”
How Can Database Observability Help Banks Prevent Transaction Failures and Downtime?
With database observability, banks can identify performance anomalies, see how databases are performing, find bottlenecks, and troubleshoot the root causes of transaction failures before they turn into major service outages. Database observability provides banking IT teams with increased visibility into queries, workloads, resource consumption, waits, blocking and performance patterns as they evolve. This proactive visibility … Continue reading “How Can Database Observability Help Banks Prevent Transaction Failures and Downtime?”
How Can Database Observability Help Retail Companies Resolve Performance Issues Faster?
Database observability for retail helps retailers detect, understand, and resolve performance problems faster by providing deep visibility into database workloads, queries, waits, anomalies, and dependencies. Instead of reacting only after applications slow down, retail IT teams can identify root causes earlier, reduce troubleshooting time, improve uptime, and strengthen database performance monitoring for retail across stores, … Continue reading “How Can Database Observability Help Retail Companies Resolve Performance Issues Faster?”
How Can Database Observability Help Telecom Companies Resolve Performance Issues Faster?
Database observability for telecom helps telecom companies detect, investigate, and resolve database performance problems faster by providing deeper visibility into queries, workloads, waits, dependencies, and anomalies. Instead of relying only on basic infrastructure metrics, teams can identify root causes sooner, reduce troubleshooting time, improve service reliability, and support stronger telecom database performance across complex, high-volume … Continue reading “How Can Database Observability Help Telecom Companies Resolve Performance Issues Faster?”