SQL HAVING Explained: Filter Grouped Data With Ease

If you’ve ever tried to filter aggregated results in SQL using WHERE, you probably ran into limitations. This is because WHERE works before grouping. For filtering after aggregation, SQL provides the HAVING clause.

This guide walks through how HAVING works, practical scenarios for its use, and answers common questions developers face.

Example Scenarios for SQL HAVING

Find Duplicate Entries

SELECT name
FROM products
GROUP BY name
HAVING COUNT(*) > 1;

This identifies product names that appear multiple times.

Filter Groups by Average Value

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING avg_salary > 50000;

Returns departments where the average salary exceeds $50,000.

Combine Multiple Conditions

SELECT department
FROM employees
GROUP BY department
HAVING COUNT(*) > 5 AND MAX(salary) > 60000;

FAQ

Does HAVING work without GROUP BY?

No. HAVING is designed to filter after grouping.

Difference between WHERE and HAVING?

WHERE filters before grouping; HAVING filters after.

Can aggregate functions be used?

Yes. Functions like SUM, AVG, and COUNT are standard in HAVING.

Is HAVING slower than WHERE?

It can be on large datasets since it operates post-aggregation.

Conclusion

The SQL HAVING clause is an essential part of working with grouped data. It allows filtering on aggregate results, something WHERE cannot do. Whether identifying duplicates, applying conditions to summaries, or narrowing down grouped datasets, HAVING gives you the flexibility you need.

By learning how to use HAVING effectively, you’ll write cleaner and more powerful queries that handle complex aggregation scenarios with ease. For detailed use cases and best practices, see the SQL HAVING Clause: The Ultimate Guide.