Best Practices for Writing Efficient SQL Queries

Efficient SQL queries are crucial for improving database performance and ensuring that applications run smoothly, especially as data volumes grow. Poorly written queries can lead to slow response times, high server load, and increased costs. In this article, we’ll explore the best practices for writing efficient SQL queries to enhance performance and optimize resource usage.

1. Use SELECT Only What You Need

Avoid using SELECT * in your queries unless you need all the columns. Retrieving unnecessary data increases memory usage and processing time.

Example: ❌ Bad:

SELECT * FROM employees;        

✅ Good:

SELECT id, first_name, last_name, department FROM employees;        

👉 This reduces the data fetched from the database, making the query faster and more efficient.

2. Use Proper Indexing

Indexes help the database engine locate data faster. Create indexes on columns that are frequently used in WHERE, JOIN, and ORDER BY clauses.

Example:

 CREATE INDEX idx_employees_department ON employees(department);        

👉 This helps the database avoid full table scans and improves query execution speed.

3. Avoid Using Functions on Indexed Columns in WHERE Clauses

When you apply a function to an indexed column, the database engine might ignore the index, leading to slower performance.

Example: ❌ Bad:

SELECT * FROM employees WHERE UPPER(last_name) = 'SMITH';        

✅ Good:

SELECT * FROM employees WHERE last_name = 'Smith';        

👉 Keep indexed columns in their original form to ensure the index is used.

4. Use EXISTS Instead of IN for Subqueries

EXISTS is generally more efficient than IN for subqueries because it stops processing once a match is found, while IN processes all values.

Example: ❌ Bad:

SELECT * FROM employees 
WHERE department_id IN (SELECT department_id FROM departments WHERE active = 1);        

✅ Good:

SELECT * FROM employees e 
WHERE EXISTS (
    SELECT 1 FROM departments d 
    WHERE d.department_id = e.department_id AND d.active = 1
);        

👉 EXISTS improves performance by stopping once the first match is found.

5. Minimize the Use of DISTINCT

DISTINCT forces the database to sort and remove duplicates, which can be expensive for large datasets. Only use it when necessary.

Example: ❌ Bad:

SELECT DISTINCT department FROM employees;        

✅ Good:

SELECT department FROM employees GROUP BY department;        

👉 Grouping is often more efficient than using DISTINCT for removing duplicates.

6. Use JOINs Instead of Subqueries When Possible

JOINs are usually more efficient than subqueries because they allow the database to build a single execution plan.

Example: ❌ Bad:

SELECT * FROM employees 
WHERE department_id = (SELECT department_id FROM departments WHERE name = 'Sales');        

✅ Good:

SELECT e.* FROM employees e
INNER JOIN departments d ON e.department_id = d.department_id
WHERE d.name = 'Sales';        

👉 JOINs reduce query complexity and improve performance.

7. Optimize ORDER BY and GROUP BY

Sorting data can be resource-intensive, so avoid ordering or grouping unless necessary. Also, use indexed columns in ORDER BY and GROUP BY to speed up processing.

Example:

SELECT department, COUNT(*) 
FROM employees 
GROUP BY department 
ORDER BY department;        

CopyEdit

SELECT department, COUNT(*) FROM employees GROUP BY department ORDER BY department;

👉 This works better when the department column is indexed.

8. Limit the Number of Rows Returned

Use LIMIT to control the number of rows returned, especially when displaying data to users. This reduces memory usage and speeds up query response time.

Example:

SELECT * FROM employees 
ORDER BY hire_date DESC 
LIMIT 10;        

👉 Limiting results helps improve UI response times and reduces server load.

9. Analyze Execution Plans

Use EXPLAIN or EXPLAIN PLAN to analyze how the database executes your query. This helps identify slow parts of the query and suggests areas for improvement.

Example:

EXPLAIN SELECT * FROM employees WHERE department = 'Sales';        

👉 Execution plans show whether indexes are used and where full table scans occur.

10. Use Transactions for Bulk Operations

When performing bulk inserts, updates, or deletes, wrap them in a transaction. This reduces overhead and improves performance by minimizing disk writes.

Example:

BEGIN TRANSACTION;
UPDATE employees SET salary = salary * 1.1 WHERE department = 'Sales';
COMMIT;        

👉 Transactions ensure consistency and improve the efficiency of bulk operations.

Conclusion

Writing efficient SQL queries is essential for maintaining fast and scalable database systems. By following these best practices — like limiting result sets, using indexes, avoiding subqueries, and analyzing execution plans — you can significantly improve query performance and database health. Regularly reviewing and optimizing queries helps prevent bottlenecks and keeps your applications running smoothly.