SQL Best Practices: 9 Tips for Writing Maintainable and Efficient Queries

August 21, 2024 | Posted in SQL

Writing maintainable and efficient SQL queries is crucial for database performance and readability. This guide walks you through a few important best practices to implement to achieve both goals—ensuring your queries are optimized and easy to manage.

1. Understand Your Data and Schema

Before writing a query, familiarize yourself with the database schema, data types, and relationships. This foundational knowledge helps in crafting precise and efficient queries.

Common Issue: Misunderstanding the schema can lead to inefficient joins, incorrect data retrieval, and performance bottlenecks.
Solution: Use schema diagrams and documentation. ERD (Entity-Relationship Diagram) tools can be beneficial for this purpose. You can use ERD tools to denote objects or concepts in the database, get definitions of entities properties (key attributes, composite attributes, etc.), and establish connections or associations between entities (e.g., relationships).

Database Diagram Example

SQL Database Diagram

2. Use Proper Indexing

Indexing columns that are frequently used in WHERE clauses, JOINs, and ORDER BY clauses can significantly speed up query performance.

Common Issue: Over-indexing can lead to increased storage and slower write operations.
Solution: Regularly review and optimize indexes. Use tools like SQL Display Estimated Execution Plan or EXPLAIN in MySQL to analyze query performance.

Before: Inefficient Query

First, let’s use a query that performs filtering inefficiently.

Before Inefficient SQL Query 1

This query filters rows based on ProductID but without an index on ProductID, resulting in a full table scan.

After: Efficient Query

Now, let’s create an index on ProductID and use the same query to show improved performance.

After Efficient SQL Query 1

With an index on ProductID, the database can quickly locate the rows matching the condition, significantly improving performance.

3. Avoid SELECT *

Only specify the columns you need in your SELECT statement. This reduces the amount of data transferred and improves performance.

Common Issue: SELECT * retrieves unnecessary data, increasing load time and resource usage.
Solution: Always list the required columns explicitly.

Before: Inefficient Query

Before Inefficient SQL Query 2

After: Efficient Query

After Efficient SQL Query 2

4. Use Joins Efficiently

Understand the differences between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN. Choose the appropriate join type based on your data requirements.

Common Issue: Using the wrong join type can result in incorrect data and poor performance.
Solution: Analyze the data relationships and use INNER JOIN for matching rows, LEFT JOIN for all rows from the left table, etc.

Understanding the differences between JOIN types is also crucial for selecting the right one for your query:

INNER JOIN: Returns only the rows where there is a match in both tables. Use this JOIN type when you need to find records that have corresponding data in both tables.
LEFT JOIN (or LEFT OUTER JOIN): Returns all rows from the left table and the matched rows from the right table. Use this JOIN when you want to include all records from the left table, regardless of whether there’s a matching row in the right table.
RIGHT JOIN (or RIGHT OUTER JOIN): Returns all rows from the right table and the matched rows from the left table. Use this JOIN type when you want to include all records from the right table, even if there’s no matching row in the left table.
FULL JOIN (or FULL OUTER JOIN): Returns all rows when there is a match in either the left or right table. Use this JOIN when you need to combine all records from both tables, regardless of whether they have a match.

Before: Inefficient Query

Before Inefficient SQL Query 3 This query will retrieve all rows from the Employee table, performs a LEFT JOIN with the Department table, and then filters out the rows where d.Name is NULL. This results in unnecessary data retrieval and processing.

After: Efficient Query

Now use the EXPLAIN plan for the optimized INNER JOIN query. The updated query will directly retrieve only the matching rows from both tables, which is more efficient.

5. Filter Data Early

Apply filters in the WHERE clause as early as possible to reduce the data set being processed.

Common Issue: Delaying filters or using them in subqueries can lead to larger intermediate result sets and slower performance.
Solution: Place WHERE clauses in the main query or as early as possible in subqueries.

Before: Inefficient Query

After: Efficient Query

6. Use Subqueries and CTEs Wisely

Common Table Expressions (CTEs) and subqueries can simplify complex queries but use them judiciously to avoid performance issues.

Common Issue: Overusing subqueries can lead to nested loops and slow performance.
Solution: Use CTEs for better readability and maintainability when dealing with complex queries.

Before: Inefficient Query

This query joins Production.Product with Sales.SalesOrderDetail and aggregates the total order quantity for each product. The HAVING clause filters products with a total order quantity greater than 100.

The query logic is embedded directly in the SELECT statement, making it more difficult to read and maintain. The nested subquery structure can become complex and cumbersome, especially as the query grows in complexity.

After: Efficient Query

The CTE named ProductOrderTotals calculates the total order quantity for each product. The main SELECT statement then filters the results based on the precomputed total order quantities.

Benefits:

Readability: The use of a CTE breaks down the query into more manageable parts, making it easier to understand the logic. The CTE clearly defines the intermediate results, improving the readability of the overall query.
Maintainability: The CTE structure allows for easier modifications. If the logic for calculating TotalOrderQty needs to change, it can be done within the CTE without altering the main SELECT statement.
Reusability: The CTE can be referenced multiple times within the same query if needed, promoting code reuse and consistency.

7. Optimize JOIN Order

The order of tables in JOIN operations can affect query performance. Place the table with the smallest result set first.

Common Issue: Poorly ordered joins can result in large intermediate result sets and slow performance.
Solution: Use EXPLAIN plans to analyze and optimize join orders.

Before: Inefficient Query

In this example, we’ll use an unnecessary join, leading to inefficiency.

The query uses multiple LEFT JOINs, which can retrieve more data than necessary. It joins EmployeeDepartmentHistory even though it’s not needed for the final result, resulting in inefficiency.

After: Efficient Query

In this optimized example, we remove the unnecessary join and use an ‘INNER JOIN’.

The query uses an INNER JOIN directly between Employee and Department, eliminating the unnecessary join with EmployeeDepartmentHistory. This results in a more efficient query, retrieving only the necessary data.

8. Handle NULLs Appropriately

Be cautious with NULL values in your queries, especially in WHERE clauses and JOIN conditions.

Common Issue: NULL values can lead to unexpected results and complicate query logic.
Solution: Use IS NULL and IS NOT NULL conditions to handle NULL values explicitly.

Before: Inefficient Query

In this example, we’ll use an inefficient way to handle NULLs in a query.

The query uses the OR operator to handle NULL values, which can result in a full table scan and slower performance. It checks each row twice: once for p.Color = ‘Red’ and once for p.Color IS NULL.

After: Efficient Query

In this optimized example, we’ll use the COALESCE function to handle NULLs more efficiently.

The query uses the COALESCE function to replace NULL values with ‘Red’, allowing the use of a single condition in the WHERE clause. This approach simplifies the query and can lead to better performance by reducing the complexity of the condition.

9. Use Transactions for Critical Operations

Enclose critical operations in transactions to ensure data integrity and consistency.

Common Issue: Lack of transactions can lead to partial updates and data corruption.
Solution: Use BEGIN TRANSACTION, COMMIT, and ROLLBACK to manage critical operations.

Before: Without Transaction

The queries update the CustomerID for specific SalesOrderIDs. The first update sets CustomerID to 2 for SalesOrderID 1, and the second update sets CustomerID to 3 for SalesOrderID 2.

If an error occurs after the first update but before the second update, the database will be left in an inconsistent state. For example, if the system crashes or if there is a constraint violation, the first update will be committed, but the second update will not. This partial update can cause data integrity issues.

After: With Transaction

The updates are enclosed within a transaction using BEGIN TRANSACTION and COMMIT.

Notes:

BEGIN TRANSACTION: This statement starts a new transaction.
COMMIT: This statement commits the current transaction, making all changes permanent.
ROLLBACK (not shown): If an error occurs, you can use ROLLBACK to undo all changes made in the current transaction.

Benefits:

Atomicity: The transaction ensures that either both updates are applied, or none are. If any statement within the transaction fails, the entire transaction can be rolled back, ensuring that the database remains in a consistent state.
Consistency: By using transactions, you ensure that all updates related to a single operation are completed successfully, preserving the consistency of the data.
Isolation: Transactions ensure that intermediate states are not visible to other operations, preventing potential conflicts or anomalies.
Durability: Once the transaction is committed, the changes are permanent, even in the event of a system failure.

Error Handling With Transaction

To further enhance the robustness of the transaction, you can include error handling with TRY…CATCH:

Additionally, using transactions to ensure data consistency and robustness further enhances the reliability of your database operations. Incorporating these practices into your development workflow will lead to more efficient, reliable, and easier-to-maintain SQL code.

Putting These Best Practices in Action

All the tips and best practices I’ve shown can be tested and applied using the AdventureWorks database, which is a well-known sample database provided by Microsoft. By practicing these techniques with AdventureWorks, you can gain hands-on experience and see firsthand the benefits of these SQL best practices.

Implement these tips in your projects, and you’ll be on your way to becoming a more proficient and effective SQL developer.

Happy querying!

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