10 Python SQL Best Practices

Python is a powerful programming language that is widely used for data analysis and automation. It is also used to interact with databases, such as SQL. When working with SQL databases, it is important to follow best practices to ensure that your code is efficient, secure, and maintainable.

In this article, we will discuss 10 Python SQL best practices that you should follow when working with SQL databases. We will cover topics such as using prepared statements, avoiding SQL injection, and using transactions. Following these best practices will help you write better code and make your applications more secure.

1. Use the connection.autocommit attribute to control transactions

When using Python SQL, it’s important to use transactions to ensure data integrity. Transactions allow you to group multiple operations into a single unit of work that either succeeds or fails as a whole. This is especially important when dealing with databases that support concurrent access from multiple users.

The connection.autocommit attribute allows you to control whether the database will automatically commit changes after each statement or wait until an explicit COMMIT command is issued. By default, this attribute is set to False, meaning that all statements are grouped together in a transaction and must be explicitly committed before they take effect. Setting this attribute to True can help improve performance by reducing the number of round trips between your application and the database.

2. Use a context manager for your cursor and connections

A context manager is a way to ensure that your database connections and cursors are properly closed when you’re done with them. This helps prevent memory leaks, which can cause performance issues in your application. It also ensures that any changes made to the database are committed or rolled back as needed.

Using a context manager for your cursor and connection objects is easy. All you need to do is wrap your code in a “with” statement like this:

with conn.cursor() as cur:
# Do something with the cursor

This will automatically close the cursor and connection when the block of code finishes executing.

3. Use a single SQL statement per cursor.execute() call

When you use multiple SQL statements in a single cursor.execute() call, it can be difficult to debug and optimize your code. It also makes it harder for other developers to understand what is happening in the code. Additionally, if one of the SQL statements fails, then all of them will fail, which could lead to unexpected results.

By using a single SQL statement per cursor.execute() call, you make it easier to debug and optimize your code, as well as making it more readable for other developers. This practice will help ensure that your code runs smoothly and efficiently.

4. Avoid using string concatenation or interpolation in your SQL statements

String concatenation and interpolation can lead to SQL injection attacks, which are a type of attack where malicious code is inserted into your database. This can be done by inserting malicious strings into the query string that will then be executed as part of the query.

To avoid this, use parameterized queries instead. Parameterized queries allow you to pass in parameters separately from the query string, so there’s no risk of malicious code being injected into the query. This makes it much more secure and also helps improve performance since the same query can be reused with different parameters.

5. Use Python variables in your SQL queries by using placeholders

Using placeholders in your SQL queries helps to prevent SQL injection attacks. This is because the placeholder will be replaced with a value from the Python variable, and not directly from user input. This means that any malicious code or characters entered by the user won’t be executed as part of the query.

Placeholders also make it easier to read and debug your code. By using placeholders, you can clearly see which parts of the query are static and which parts are dynamic. This makes it much easier to identify potential issues when debugging.

6. Don’t use %s as a placeholder for table names, column names, etc.

When using %s as a placeholder, the SQL query is vulnerable to injection attacks. This means that an attacker can inject malicious code into your database by manipulating the parameters of the query. To prevent this from happening, use parameterized queries instead. Parameterized queries allow you to specify which values are allowed in the query and which ones are not. This makes it much harder for attackers to manipulate the query and inject malicious code.

7. Use the fetchall(), fetchmany(), or fetchone() methods of a cursor class to retrieve rows from a query result

Using the fetchall() method allows you to retrieve all of the rows from a query result in one go, which is much more efficient than looping through each row individually. The fetchmany() and fetchone() methods allow you to specify how many rows you want to retrieve at once, so you can control the amount of data that’s being retrieved from the database. This helps reduce the load on your system by limiting the number of queries that need to be executed.

8. Close the database connection after work completes

When you open a connection to the database, it uses up resources on both the server and client side. If you don’t close the connection after work is done, those resources will remain in use until the connection times out or is manually closed. This can lead to performance issues as more connections are opened than necessary.

Closing the connection also helps ensure that any changes made during the session are committed to the database. Without closing the connection, these changes may not be saved.

To make sure your Python SQL code follows this best practice, always include a line of code at the end of your script that closes the connection.

9. Consider using an ORM (Object Relational Mapper) like SQLAlchemy instead of writing raw SQL

An ORM like SQLAlchemy allows you to write Python code that is more readable and maintainable than raw SQL. It also provides a layer of abstraction between your application and the database, making it easier to switch databases if needed in the future. Additionally, an ORM can help reduce the amount of time spent writing and debugging SQL queries by providing helpful features such as object-relational mapping, query building, and data validation.

10. If you must write raw SQL, consider using a library such as sqlparse to help with formatting

SQL is a powerful language, but it can be difficult to read and understand. By using a library such as sqlparse, you can make your SQL code easier to read by formatting it in an organized way. This makes it easier for other developers to review and debug the code. Additionally, this helps ensure that all of your queries are written correctly and efficiently.

Using a library like sqlparse also allows you to write more complex queries without having to worry about syntax errors or typos. It’s important to remember that when writing raw SQL, it’s easy to make mistakes that could lead to unexpected results or even security vulnerabilities. Using a library like sqlparse can help reduce these risks.