SQL (Structured Query Language) is the backbone of managing and retrieving data from relational databases. Writing efficient and optimized SQL queries is crucial for maintaining good database performance and scalability. In this blog, we will explore the top 10 SQL query best practices with real-world examples to illustrate their importance and impact.

1. Use Indexes Wisely

Indexes are critical for enhancing query performance, especially when dealing with large datasets. Let’s consider a real-world example with a table named “employees” containing a significant number of records:

// sql

// Create an index on the "department_id" column

CREATE INDEX idx_department_id ON employees (department_id);

The above query creates an index on the “department_id” column. With this index in place, queries that involve filtering or joining based on the “department_id” will run significantly faster.

2. Minimize the Use of SELECT *

Using the wildcard (*) in SELECT statements should be avoided, especially for large tables. Instead, explicitly list the required columns:

// sql

// Bad practice (avoid using SELECT *):

SELECT * FROM employees WHERE department_id = 100;

// Good practice (explicitly list required columns):

SELECT employee_id, first_name, last_name FROM employees WHERE department_id = 100;

By specifying only the necessary columns, we reduce unnecessary data retrieval, leading to improved query performance and reduced network overhead.

3. Use Joins Efficiently

Efficiently using joins is essential for retrieving data from multiple tables. Let’s consider a scenario where we have two tables, “employees” and “departments,” and we want to fetch the employees along with their department names:

// sql

// Bad practice (inefficient join):

SELECT e.*, d.department_name 

FROM employees e, departments d

WHERE e.department_id = d.department_id;

// Good practice (use explicit JOIN):

SELECT e.*, d.department_name 

FROM employees e

JOIN departments d ON e.department_id = d.department_id;

Using the explicit JOIN syntax enhances query readability and ensures that the query planner understands the join conditions properly.

4. Limit the Results with WHERE

Using the WHERE clause efficiently helps in limiting the number of rows returned by a query. Consider the following example where we want to retrieve employees with a salary greater than 50000:

// sql

// Bad practice (inefficient use of WHERE):

SELECT * FROM employees WHERE salary > 50000;

// Good practice (limit results with WHERE):

SELECT employee_id, first_name, last_name, salary

FROM employees

WHERE salary > 50000;

By specifying only the required columns and filtering with the WHERE clause, we reduce the data size and improve query performance.

5. Avoid Using Subqueries Whenever Possible

Subqueries can be useful, but they often impact query performance. Let’s look at an example where we want to retrieve employees with salaries greater than the average salary:

// sql

// Bad practice (using subquery):

SELECT * 

FROM employees

WHERE salary > (SELECT AVG(salary) FROM employees);

// Good practice (use a common table expression, CTE):

WITH avg_salary AS (

    SELECT AVG(salary) AS avg_sal FROM employees

)

SELECT employee_id, first_name, last_name, salary

FROM employees

JOIN avg_salary ON salary > avg_sal;

Using a common table expression (CTE) or other alternatives to subqueries can lead to better query optimization.

6. Be Cautious with DISTINCT

Using DISTINCT can be resource-intensive, so use it judiciously. Consider a scenario where we want to retrieve unique department IDs from the employees table:

// sql

// Bad practice (inefficient use of DISTINCT):

SELECT DISTINCT department_id FROM employees;

// Good practice (use GROUP BY):

SELECT department_id FROM employees GROUP BY department_id;

In this example, using GROUP BY achieves the same result without the overhead of DISTINCT.

7. Optimize String Comparisons

String comparisons can be slow, especially when dealing with large datasets. Let’s see an example where we want to retrieve employees whose first name is ‘John’:

// sql

// Bad practice (inefficient string comparison):

SELECT * FROM employees WHERE first_name = 'John';

// Good practice (use case-insensitive collation):

SELECT * FROM employees WHERE first_name = 'John' COLLATE SQL_Latin1_General_CP1_CI_AS;

Using a case-insensitive collation ensures efficient indexing and comparison for string values.

8. Use UNION All Instead of UNION

UNION combines the results of two or more SELECT statements, removing duplicates by default. However, if duplicates are not a concern, using UNION ALL is more efficient:

// sql

// Bad practice (inefficient use of UNION):

SELECT employee_id FROM employees WHERE department_id = 100

UNION

SELECT employee_id FROM employees WHERE department_id = 200;

// Good practice (use UNION ALL):

SELECT employee_id FROM employees WHERE department_id = 100

UNION ALL

SELECT employee_id FROM employees WHERE department_id = 200;

Using UNION ALL avoids the overhead of duplicate elimination and results in faster query execution.

9. Keep Transactions Short and Sweet

Transactions should be kept as short as possible to avoid unnecessary locks and enhance database concurrency. For example:

// sql

// Bad practice (lengthy transaction):

BEGIN TRANSACTION;

UPDATE employees SET salary = salary * 1.1 WHERE department_id = 100;

// other operations...

COMMIT;

// Good practice (short transaction):

UPDATE employees SET salary = salary * 1.1 WHERE department_id = 100;

Short transactions reduce the holding time for locks, minimizing the impact on other database operations.

10. Regularly Maintain and Optimize the Database

Regular maintenance tasks are crucial for optimal database performance. Consider scheduling tasks such as index reorganization, database backups, and statistics updates:

// sql

// Reorganize index:

ALTER INDEX idx_department_id ON employees REORGANIZE;

// Update statistics:

UPDATE STATISTICS employees;

Proper maintenance ensures that the database remains efficient over time, even as data grows.

Conclusion

Following these top 10 SQL query best practices with real-world examples can significantly improve the performance and scalability of your database systems. By optimizing your SQL queries and adhering to best practices, you can ensure that your applications run smoothly and efficiently, even with large datasets. Remember to regularly analyze query performance, fine-tune your queries, and maintain your database to achieve the best possible results in the long run. Happy querying!