Choosing the right database for your application is important, especially for backend applications that interface directly with the database. Many options are available, each suited to certain use cases and with different prices, speeds, and scaling options.

Python is typically used in data-heavy applications because it has powerful libraries for data manipulation, so the database you use for a Python application is important.

This article compares MariaDB to other database technologies like MySQL and PostgreSQL and discusses why it’s a great choice when using Python.

What Makes MariaDB So Great for Python?

Thanks to its powerful data manipulation and machine learning libraries, Python is a popular programming language for data practitioners — especially in data engineering and data science. Both fields deal with large amounts of data, increasing the storage required and memory and processing power needs.

Licensing costs often increase along with your data size, so it makes sense to use an open-source database. You can also save money on hardware. Because you’re in total control of where the application is deployed, you can find the cheapest solutions in the cloud or on-premises.

Many open-source databases exist, including relational, NoSQL, and graph databases. Relational databases are generally great for structure and consistency, whereas NoSQL databases, which enable rapid development, are designed to be more flexible.

Although Python works perfectly fine with NoSQL databases, relational databases are better suited to the analytics and large-scale data processing that are common uses for Python.

Let’s compare MariaDB with some other popular open-source relational databases.

MariaDB vs MySQL vs PostgreSQL

The most popular open-source relational databases are MariaDB, SQLite, MySQL, and PostgreSQL. Each has a similar set of core technical capabilities, but MariaDB has some unique features that make it best suited for Python applications.

Moreover, MariaDB is open-source, so its features don’t depend on an external entity like MySQL, which Oracle now owns. Although the open-source community can still suggest and add features to MySQL, Oracle (which has a competing database) decides where it’s going.

On the other hand, MariaDB was created after Oracle acquired MySQL to continue a community-led approach to development. As a result, MariaDB has a richer feature set, including more storage engines that ensure better performance for queries and replication than MySQL. This improved performance is important when working with large data sets.

The reasons for using MariaDB over PostgreSQL are a little more nuanced, as PostgreSQL is also community-driven. However, PostgreSQL uses its own BSD-like license, the PostgreSQL License, which is more permissive than MariaDB’s GNU license and allows users to create closed-source extensions.

While they benefit the user creating the extension, these features aren’t always used in the core PostgreSQL database, and developers can even charge for using their extensions. MariaDB and MySQL GNU’s licensing does not allow new features to be privatized — all new functionality is freely available.

MariaDB and PostgreSQL have the richest feature sets. However, MariaDB has some convenient features for a Python backend. For example, MariaDB uses one language to interface with all its different storage engines — OLAP and OLTP systems are controlled with the same syntax, reducing the burden on developers.

Your Python backend can write transaction-like data to MariaDB, which can copy that data to a storage engine more suited to analytical queries. Developers can write analytical queries against the replica using the same syntax for improved performance.

MariaDB also recently introduced f-String-like formatting for strings, similar to Python. This saves developers using MariaDB with Python from mentally switching between languages.

MariaDB Python Connector

Before 2020, Python programmers connected to MariaDB through the MySQL Python package. This was possible because MariaDB is a fork of MySQL, but it meant that MariaDB connections behaved the same way as MySQL.

In 2020, a native MariaDB connector was released to remove the reliance on MySQL, giving more control to the MariaDB community. You can install the connector with pip, Python’s package manager, and use it for all common CRUD use cases.

All statements are managed through a cursor object. By default, the MariaDB cursor accepts queries as prepared statements so you can sanitize any dynamic parts of the query. This is safer than formatting strings to build queries, which leaves your application vulnerable to SQL injection attacks.

How the Connector Works

The connector is simple to use. First, you import the MariaDB connector library into your application and use the following function to connect to your MariaDB database server:

import mariadb

try:
    connection = mariadb.connect(
        user=username,
        password=password,
        host=mariadb_host,
        port=3306,
        database="sales"

    )
except mariadb.Error as err:
    print(f"An error occurred whilst connecting to MariaDB: {err}")

Once connected, all queries are executed through a cursor object. You obtain the cursor object, then use it to submit queries.

cursor = conn.cursor()

To submit a query as a prepared statement, use a question mark as a placeholder in the query text and pass the required values in as a tuple.

ur.execute( "SELECT * FROM sales WHERE sale_date >= ? and price > ?", (sale_date_val, price_val))

The input values from the tuple replace the question marks in order from left to right. These input values are also sanitized to prevent SQL injection. Built-in protection from SQL injection is beneficial for a language like Python, designed with beginner programmers in mind.

Python and MariaDB for WordPress

Another benefit of using MariaDB as a backend for your Python application is that it’s easy to connect the database to a WordPress frontend, especially when using Kinsta.

Kinsta supports MariaDB as a backend database, making them instantly compatible. Your WordPress site can easily access data sets processed in Python. For example, you can perform some analysis in Python, store the results in MariaDB, and then display them as a graph on a WordPress page.

Kinsta also provides a development platform called DevKinsta for developing an end-to-end solution. With DevKinsta, you can use your local machine to set up a WordPress site with a MariaDB backend, which can then be pushed to Kinsta once it’s ready to go live. This integration makes setting up a site with a MariaDB database simple — your website can be live with just a few clicks.

Summary

Multiple open-source relational databases, including MariaDB, MySQL, and Postgres, can serve as a Python backend. However, MariaDB is the most flexible and feature-rich option, thanks to its open-source nature.

When working with large datasets, MariaDB provides many storage engines, making it faster than the alternatives, and supports multiple use cases, from transactional processing to analytical queries. The flexibility, speed, and native Python integration with the MariaDB Python connector make it a great choice as a backend for Python applications that process large data sets.

Moreover, MariaDB can plug directly into a WordPress frontend, making your datasets accessible to your website. Kinsta’s MariaDB support makes this integration smoother. With DevKinsta, you can configure your WordPress site to use MariaDB on your local machine before deploying the solution via Kinsta.

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Jeremy Holcombe Kinsta

Senior Editor at Kinsta, WordPress Web Developer, and Content Writer. Outside of all things WordPress, I enjoy the beach, golf, and movies. I also have tall people problems.