Equal and not equal in SQL: Comparison Operators 

When working with databases, one of the most essential skills is writing effective SQL queries. At the heart of these queries are comparison operators, which allow you to filter, match, or contrast data values. Among these operators, the most commonly used are those that check whether values are equal or not equal. While it may seem straightforward, understanding how these operators function — especially with different data types and NULL values — can significantly improve your ability to work with data efficiently.

TL;DR:

SQL comparison operators are powerful tools for filtering and querying databases. The = (equal) and or != (not equal) operators are used to compare values in SQL statements. These operators play crucial roles in WHERE clauses and join conditions. It’s also important to handle NULLs wisely because normal equality tests do not work as expected with them.

Understanding SQL Comparison Operators

SQL provides several comparison operators that are used to evaluate the relationship between two expressions. These operators return a Boolean value: TRUE, FALSE, or UNKNOWN. Here’s a quick list of the most common comparison operators:

• = : Equal To
• or != : Not Equal To
• < : Less Than
• > : Greater Than
• <= : Less Than or Equal To
• >= : Greater Than or Equal To

While each of these has its use, in this article, we’ll focus on the equal and not equal operators — how they are used, how they behave with different data types, what happens with NULL values, and best practices for writing robust comparisons.

The Equal Operator: =

The equal operator is used to compare two values for exact equivalence. It’s commonly seen in SELECT, UPDATE, or DELETE statements when filtering rows.

Here’s a simple example:

SELECT * FROM employees WHERE department = 'Sales';

This query retrieves all records from the employees table where the department is exactly ‘Sales’.

Things to note:

• Matching text values: SQL is case-insensitive in most systems when comparing strings, though this can vary (e.g., PostgreSQL treats case differently unless specifically configured).
• Matching numbers: Long as types are compatible, numeric comparison is straightforward.

The Not Equal Operators: and !=

Both and != are used to check that two values are not equal. Although is the standard SQL syntax, != is widely supported in most database systems like MySQL, SQL Server, and PostgreSQL.

Here’s a use-case example:

SELECT * FROM customers WHERE country != 'USA';

This query returns all customers whose country is not ‘USA’. It’s straightforward, but there are a few caveats you should be aware of, especially when NULL values are involved.

Handling NULLs in Equality Checks

This is where things get interesting. SQL uses a three-valued logic system: TRUE, FALSE, and UNKNOWN. When a NULL is involved in any comparison, the result is usually UNKNOWN—not TRUE or FALSE. This can lead to unexpected omissions in query results.

For example:

SELECT * FROM orders WHERE status != 'Shipped';

This will NOT return orders where the status is NULL, even though NULL is certainly not ‘Shipped’ — because NULL is not considered anything, including not equal.

To include NULL values explicitly, you’ll need to use the IS NULL operator:

SELECT * FROM orders WHERE status != 'Shipped' OR status IS NULL;

If you’re frequently dealing with NULL values, this pattern can make a significant difference in your query accuracy.

Best Practices

Here are some tips and best practices to keep in mind when using equal and not equal comparison operators:

• Be mindful of NULLs: Always remember that NULL is not equal to anything, and comparisons involving NULL generally evaluate to UNKNOWN.
• Use IS NULL and IS NOT NULL for NULL checks, rather than = NULL or != NULL, which will not work.
• Prefer explicit over implicit data type comparisons: When comparing numbers to strings or dates to strings, be aware of how your RDBMS performs implicit type conversion.
• Use appropriate collation settings if you’re working with case-sensitive systems like PostgreSQL.

Equal and Not Equal in JOIN Conditions

Comparison operators are not just used in WHERE clauses. They’re also vital in JOIN conditions.

Example — INNER JOIN with equality:

SELECT o.order_id, c.customer_name
FROM orders o
JOIN customers c ON o.customer_id = c.customer_id;

This join condition uses equality (=) to match records between the two tables. Conversely, you can use a not-equal condition in more advanced analyses:

SELECT a.product_id, a.category_id
FROM products a
JOIN products b ON a.category_id != b.category_id
WHERE a.price = b.price;

This query finds products with the same price but belonging to different categories.

Note: Not equal joins can be much more expensive in terms of performance, especially with large datasets. Always evaluate indexes and execution plans carefully.

How Different RDBMSs Handle NOT EQUAL

While SQL standards advocate for the use of as the not equal operator, developers often use != due to its widespread support. Here’s how different databases treat them:

• MySQL: Supports both != and
• SQL Server: Also supports both, but official docs recommend
• PostgreSQL: Both operators work, but is standard
• Oracle: Welcomes both, yet documents use the standard

If you’re writing for portability or collaboration, it’s a good habit to stick with the ANSI SQL standard form: .

Common Mistakes to Avoid

Here are some pitfalls many developers run into when using equality and inequality operators:

• Using = NULL or != NULL: As discussed earlier, these will NOT work. You must use IS NULL or IS NOT NULL.
• Implicit type mismatch: Comparing numeric fields to string literals without type casting can lead to incorrect results or errors.
• Assuming NOT EQUAL includes NULLs: NULLs are neither equal nor not equal to any value.

Conclusion

Knowing how to use equal (=) and not equal (, !=) operators in SQL is fundamental to writing precise, efficient queries. While these operators may seem basic, their correct usage — especially with complex datasets and NULL values — can greatly influence the accuracy of your data analysis and application logic.

Always remember to:

• Be cautious with NULL values in comparisons
• Stick to the SQL standard if possible for portability
• Use inequality in JOINs wisely

The key to mastering SQL lies in understanding how even the simplest operators behave under the hood — and the equal and not-equal operators are no exception.