ORM in Python

ORM or Object Relational Mapping uses an object-oriented paradigm to create, query, and manipulate data stored in a database. It is a software layer between the actual database and the applications used for database management.

An ORM lets us define database tables using programming languages, perform queries using objects and methods, and manipulate data using object-oriented programming. Behind the scenes, all the query logic is converted to SQL or Structured Query Language equivalent queries. Although the translated queries may or may not be the best for a specific action, ORMs have proven good performance for large-scale real-world applications.

ORMs make table management a very seamless task. Generally, tables are represented using a class in ORM; hence, it becomes easy to update, reuse, scale, and maintain them. ORMs handle a lot of stuff automatically, allowing developers to focus more on the application’s features.

Over the years, technology has grown massively, and these days most of the programming languages heavily used by the community have ORMs developed for them. Some programming languages have more than one ORMs on their list. In this article, we will talk about ORMs available in the market for the Python programming language.

ORMs in Python

Python is one of the fastest-growing and highly used languages across multiple domains such as web development, data science, machine learning, app development, etc. It is backed by a huge community of talented programmers and developers who keep improving, optimizing, and building new stuff such as libraries and frameworks for the language.

Although there are many ORMs available in the market for Python programming language, we will only focus on three ORMs in this article because not every ORM is robust, scalable, well developed, follows state-of-the-art practices, efficient, optimized, and offers performance.

This article will talk about three ORMs, namely, peewee, SQLAlchemy, and Storm. These three ORMs are widely used in the industry and offer great features.

peewee

The peewee ORM is a simple, expressive, and small ORM for Python. Since it is a small ORM, it is meant for small-scale applications. This ORM supports SQLite database, MySQL database, PostgreSQL database, and cockroachdb database. This ORM was developed in Python 3.6 and can be used with Python 2.7+ and 3.4+.

To learn more about the peewee ORM, refer to the official GitHub repository for documentation and code snippets here.

SQLAlchemy

The SQLAlchemy ORM is a robust SQL Toolkit for Python. This ORM delivers flexibility and the full potential of SQL to Python developers in an optimized manner. This ORM is designed for efficient and high-performance database accesses using Python. The SQLAlchemy ORM is one of the best ORMs for large-scale applications. Organizations such as Yelp!, DropBox, reddit, Survey Monkey, and The OpenStack Project use the SQLAlchemy ORM in their tech stack.

Generally, the SQLAlchemy ORM is used along with the Flask web framework in the community. Large databases bring performance issues. This ORM helps perform complex CRUD (Create, Read, Update, and Delete) queries over giant databases with ease and efficiency.

To learn more about the SQLAlchemy ORM, refer to the official documentation here.

Storm

The Storm ORM is a clean and lightweight API or Application Programming Interface that provides SQL support for the Python programming language. The Storm ORM has a short learning curve and is suitable for large-scale applications. It is a fast and flexible ORM that provides efficient access to a database and eases the task of performing complex queries over large databases. This ORM supports connecting to multiple databases using the same Python code.

TO learn more about the Storm ORM, refer to the official documentation here.