SQL NOT EQUAL Operator

The SQL NOT EQUAL Operator is a comparison operator in data filtering that eliminates values that don’t meet a given condition.

target="_blank" rel="noreferrer noopener">SQL uses the following symbolization for NOT EQUAL: != or <>. Both are accepted and are used most often in queries to return rows where the specified condition is not equal to a given value.

What is the SQL NOT EQUAL Operator?

We will learn what the SQL NOT EQUAL operator looks like and its syntax, as well as practice some examples of how it might be used to filter data. Again, we’ll discuss important topics, including Boolean values, case sensitivity, multiple conditions, and query optimization.
It compares a value of a column with a specified value and returns rows where the two are not equal. Its major use is in data filtering to omit those rows that meet a certain criterion.

SQL NOT EQUAL Operator Syntax

You may use either != or <> for the NOT EQUAL operator:

SELECT column_names
FROM table_name
WHERE column_name != value;

-- OR

SELECT column_names
FROM table_name
WHERE column_name <> value;

Example of SQL NOT EQUAL

Let’s consider a simple example using an employees table. We want to retrieve all employees who do not belong to department 10.

employees Table:

employee_id	name	department_id	salary
1	Alice	10	5000
2	Bob	20	7000
3	Carol	30	4000
4	Dave	10	6000
5	Eve	20	5000

Here’s the query using the NOT EQUAL operator:

SELECT name, department_id
FROM employees
WHERE department_id != 10;

Result:

name	department_id
Bob	20
Carol	30
Eve	20

This query filters out employees from department 10, returning only those from other departments.

SQL NOT EQUAL and Boolean Values

The NOT EQUAL operator is applicable to Boolean values also. Assume that you have a column which is of type Boolean (TRUE or FALSE). In this case, you might filter rows based on the Boolean condition, excluding rows with the NOT EQUAL operator.

Example with Boolean Values

Let’s assume we have a projects table where the completed column stores Boolean values (TRUE or FALSE):

projects Table:

project_id	project_name	completed
1	Project A	TRUE
2	Project B	FALSE
3	Project C	TRUE
4	Project D	FALSE

We want to retrieve projects that are not completed:

SELECT project_name
FROM projects
WHERE completed != TRUE;

Result:

project_name
Project B
Project D

In this query, the NOT EQUAL operator filters out completed projects, returning only those that are still ongoing.

Case Sensitivity in SQL NOT EQUAL

SQL comparison operators, for example, NOT EQUAL, suffer from case sensitivity in the database system. The default comparison of MySQL is case-insensitive. However, it is case-sensitive in PostgreSQL and other databases.

Example of Case Sensitivity

Let’s say we have a customers table:

customers Table:

customer_id	customer_name
1	Alice
2	Bob
3	alice
4	Carol

We want to retrieve all customers whose names are not equal to ‘Alice’. If case sensitivity applies, alice (lowercase) will not be considered equal to Alice (uppercase).

SELECT customer_name
FROM customers
WHERE customer_name != 'Alice';

Result (Case-Sensitive):

customer_name
Bob
alice
Carol

In a case-sensitive environment, alice (lowercase) is treated as a different value from Alice (uppercase). In a case-insensitive environment, only Bob and Carol would be returned.

Multiple Conditions with SQL NOT EQUAL

The SQL NOT EQUAL Operator can be combined with other conditions using logical operators such as AND and OR to filter data more precisely.

Example with Multiple Conditions

Suppose we want to retrieve employees who are not in department 10 and have a salary greater than 5000:

SELECT name, department_id, salary
FROM employees
WHERE department_id != 10 AND salary > 5000;

Result:

name	department_id	salary
Bob	20	7000

This query filters out employees from department 10 and returns only those who earn more than 5000.

SQL NOT EQUAL vs Other Comparison Operators

The SQL NOT EQUAL Operator belongs to the family of comparison operators that includes =, <, >, <=, >=, and LIKE. For this assignment, in case you are filtering data to fetch only rows which satisfy specific criteria or to exclude rows based on particular conditions, you could use these operators.

SQL NOT EQUAL with Subqueries

Also, the NOT EQUAL operator can be used with subqueries to dynamically filter a dataset. A subquery returns a result set that could then be compared with the main query using the NOT EQUAL operator.

Example with Subquery

Suppose we also have another table called departments, and we want to retrieve the list of employees who are not in any of the departments in this table.

departments Table:

department_id	department_name
10	HR
20	Finance

We can use a subquery to filter out employees who are part of these departments:

SELECT name
FROM employees
WHERE department_id NOT IN (SELECT department_id FROM departments);

Result:

name
Carol

This query returns employees whose department is not in the departments table.

SQL NOT EQUAL with Query Optimization

Use of the NOT EQUAL operator appropriately can make a significant difference in the performance of the query, but on huge tables; it’s extremely important to optimize queries by indexing columns frequently used in the conditions such as NOT EQUAL as well.

Query Optimization Tips:

• Indexing: Index columns involved in the NOT EQUAL conditions to enhance data retrieval performance.
• In certain scenarios, the use of NOT EQUAL can result in full table scans. It has to be optimized for such a query on its database as well.
• Examine Execution Plans: Database tools are used to examine a query execution plan and identify potential performance bottlenecks in queries with NOT EQUAL.

Advantages of SQL NOT EQUAL Operator

The SQL NOT EQUAL operator is mainly applied to apply a filter which excludes the happening of a condition given. This avoids unwanted values, creating more accurate queries and thus increasing the flexibility of SQL queries. The important benefits of the SQL NOT EQUAL (!= or <>) operator are discussed as follows:

1. Flexible Data Filtering

One of the main advantages of the NOT EQUAL operator is its flexibility in filtering data. It allows you to specify conditions to exclude specific values or records from the result set. This flexibility is especially useful when you want to focus on data that doesn’t match a particular criterion.

For example:

SELECT * 
FROM Employees 
WHERE Department != 'HR';

In this query, all employees except those in the HR department are retrieved, offering a straightforward way to filter out unwanted records.

2. Enhanced Precision in Data Retrieval

The NOT EQUAL operator allows controlling exactly what data is retrieved from a database. It’s possible to exclude exact values and thus include only the necessary information in the result set. Such control is especially useful when it’s necessary to find data which is not equal to a certain value.

For instance:

SELECT * 
FROM Products 
WHERE Price <> 100;

This query returns all products whose price is not equal to 100, enabling precise filtering based on the data criteria.

3. Simplicity and Readability in Query Structure

The NOT EQUAL operator is easy to write and read, which would make SQL query syntax much more intuitive. Use != or <> when you are querying your database, for example, to clearly and directly express exclusion conditions. This makes it easier still to make queries more readable, especially in the case where clearness is already important.

For example:

SELECT * 
FROM Orders 
WHERE Status <> 'Completed';

This query is straightforward and easy to interpret: it returns all orders that are not marked as completed. The simplicity of the NOT EQUAL operator makes it a preferred choice for many developers.

4. Versatile Across Data Types

The NOT EQUAL operator can be used with any data type: strings, numbers, date fields, etc. Such flexibility makes it a very handy operator in SQL when it comes to querying datasets and different structures. Whether text or numbers or dates that are compared, the NOT EQUAL operator is all universally efficient.

For example:

SELECT * 
FROM Employees 
WHERE HireDate <> '2024-01-01';

5. Efficient for Simple Exclusion Conditions

For instance, if you need to exclude certain values, the NOT EQUAL operator is probably among the best options for expressing the condition. Rather than using more complex operators or clauses, != or <> gives you a quick and effective way of getting rid of records from a result set.

6. Works Seamlessly with Other Logical Operators

The NOT EQUAL operator integrates smoothly with other logical and comparison operators in SQL. You can easily combine it with operators like AND, OR, IN, and BETWEEN to form more complex conditions, enhancing the power and flexibility of your queries.

7. Helps in Data Validation and Integrity Checks

The NOT EQUAL operator can be highly useful for validating data and performing integrity checks. It allows you to quickly spot anomalies or exceptions in the data, helping ensure the accuracy and consistency of your records.

8. Supports Selective Data Updates and Deletions

A NOT EQUAL operator is handy when you’ll want to update or delete those records that do not meet a certain criteria. You can, therefore, have control over updates and deletions on your data precisely because you can zero down to the records that do not match certain criteria.

Disadvantages of SQL NOT EQUAL Operator

Though useful in excluding specific values from results of a query, using the SQL NOT EQUAL operator comes with some significant drawbacks. The main disadvantages bring about effects on query performance, readability, and accuracy. Below are the key disadvantages of using the NOT EQUAL (!= or <>) operator in SQL.

1. Can Cause Performance Issues on Large Datasets

One of the significant disadvantages of the NOT EQUAL operator is that it can lead to poor query performance, particularly when dealing with large datasets. SQL databases are optimized to search for specific matching conditions, but when you use NOT EQUAL, the database has to scan through a large number of records to exclude those that match the condition. This can prevent efficient use of indexing and slow down query execution.

For example:

SELECT * 
FROM Orders 
WHERE OrderStatus != 'Completed';

If the OrderStatus column is indexed, the database might not fully utilize the index because of the negation, potentially leading to a full table scan, which can significantly impact performance on large tables.

2. Limited Use of Indexes

When you have a NOT EQUAL operator, SQL databases generally have a hard time to leveragd indexes. Equality comparisons (=) can completely be leveraged because they can retrieve data in the snap, whereas using the NOT EQUAL operator makes things confusing since it necessitates testing all values that do not match the condition rather than testing a given value.

For instance:

SELECT * 
FROM Customers 
WHERE Country <> 'USA';

Even when the column called Country is indexed, the query engine will not most probably take full advantage of an index, simply because the database must exclude one value but must include all other values, possibly leading to a performance bottleneck.

3. Handling of NULL Values

Another problem related to the application of NOT EQUAL is the behavior regarding NULL values in SQL. As described by SQL, NULL is known as a value. Comparisons made through != or <> do not include rows with NULL in a comparison column; this sometimes leads to unexpected results when performing queries because of NULL values in a database.

For example:

SELECT * 
FROM Employees 
WHERE Salary != 50000;

If any employee has a NULL salary, that record won’t be included in the result, as NULL is not considered equal or not equal to any value. This behavior can cause confusion and lead to incomplete results if not accounted for properly.

4. Reduced Query Optimization

SQL databases use optimizers to generate the most efficient execution plan for a query. However, when using the NOT EQUAL operator, the query optimizer may have difficulty producing an optimal plan. This can lead to suboptimal execution, especially for complex queries with multiple conditions.

5. Potential Confusion in Query Logic

The use of the NOT EQUAL operator can sometimes introduce logical complexity, particularly when combined with other conditions. Negating a condition can make it harder to understand what data is being excluded, especially in complex queries involving multiple AND or OR operators.

6. Increased Complexity in Maintenance

As queries become more complex, the use of the NOT EQUAL operator can lead to difficulty in maintaining the code. Excluding values in multiple conditions might lead to confusion during updates or modifications, particularly for developers who are not familiar with the original intent of the query.

7. Difficulties in Testing and Debugging

When using the NOT EQUAL operator in SQL queries, it can be more difficult to test and debug the query results. Since the condition is excluding certain values, verifying whether the query is correctly excluding the right data can be tricky, particularly when working with large datasets. Ensuring the accuracy of the exclusions often requires additional testing steps.