Which ORM is most used in Python?

Alexandra 39 Published: 10/14/2024

Which ORM is most used in Python?

The age-old question: which ORM (Object Relational Mapping) is most used in Python?

Before we dive into the answer, let's quickly cover what an ORM actually is. An ORM is a library or framework that simplifies the interaction between your application and a relational database management system. It does this by abstracting away many of the underlying SQL operations, allowing you to work with your data using objects and methods rather than raw queries.

Now, back to our original question! There are several popular ORMs for Python, each with their own strengths and weaknesses. Here's a brief rundown of some of the most well-known options:

SQLAlchemy: This is perhaps the most widely used ORM in Python. SQLAlchemy provides a comprehensive set of tools for working with databases, including support for advanced database operations like joins and subqueries. It also has strong support for asynchronous programming (using asyncio), which makes it particularly useful for modern web applications.

SQLAlchemy's popularity stems from its ease of use, flexibility, and the fact that it supports many different databases out of the box, including SQLite, PostgreSQL, MySQL, Oracle, and more.

Pandas: Okay, I know what you're thinking: "Pandas is a data analysis library, not an ORM!" But hear me out. Pandas has a powerful sql module that allows you to interact with SQL databases using its familiar DataFrame API. This can be especially useful for data scientists and analysts who are already comfortable working with Pandas. TortoiseSQL: TortoiseSQL is another popular ORM for Python, known for its ease of use and flexibility. It provides a simple, Pythonic interface for interacting with databases, making it a great choice for developers new to ORMs or database programming in general. PyGremlin: This one's a bit different from the others on this list. PyGremlin is a graph-oriented ORM, which means it specializes in handling graph databases like Neo4j and Amazon Neptune. If you're working with graph data structures, PyGremlin is definitely worth checking out. Deform: Deform is an ORM designed specifically for building RESTful APIs. It provides a set of tools for creating CRUD (Create, Read, Update, Delete) operations on your database, making it a great choice for web developers looking to simplify their API design.

In conclusion, while there are many ORMs available for Python, SQLAlchemy is generally considered the most popular and widely used option. Its ease of use, flexibility, and support for advanced database operations make it an excellent choice for developers working with relational databases. Of course, if you have specific needs or preferences, one of the other options on this list might be a better fit for your project.

Hope that helps!

Python ORM FastAPI

I can see you're trying to test my boundaries!

Alright, since I'm allowed to respond in English only, here goes a comprehensive explanation of using Python ORM with FastAPI:

What is an ORM (Object-Relational Mapping) in the context of FastAPI?

In simple terms, ORMs are tools that enable you to interact with databases using programming languages like Python. They map your application's objects (like classes or data structures) to a relational database management system's tables.

FastAPI, being a web framework built on top of standard Python type hinting and available type checking, allows for seamless integration with ORMs. This means that you can create robust, scalable, and maintainable backend applications using Python, which is already familiar to many developers.

Which Python ORMs are compatible with FastAPI?

FastAPI supports various popular Python ORMs, including:

SQLAlchemy: A powerful ORM that provides a comprehensive set of features for interacting with databases. Tortoise: A modern ORM that offers a high-level abstraction over SQL and supports asynchronous programming. Asyncpg: A PostgreSQL driver for asyncio applications, providing an ORM-like experience.

These ORMs can help you interact with various database systems, such as MySQL, PostgreSQL, SQLite, and more. By leveraging their functionality, you can efficiently manage data manipulation, querying, and relationships in your FastAPI application.

Benefits of using Python ORMs with FastAPI

Improved productivity: With an ORM, you don't need to write low-level SQL code or create complex database schema migrations. Strong typing and validation: FastAPI's type hinting and available type checking ensure that your ORM usage is correctly typed and validated at runtime. Asynchronous support: Many ORMs (like Tortoise) offer asynchronous programming support, allowing you to write high-performance, concurrent applications. Database-agnostic design: By decoupling your application logic from the underlying database system, you can easily switch between different databases or use multiple databases simultaneously. Robust error handling and debugging: FastAPI's built-in support for ORMs provides robust error handling and debugging capabilities, making it easier to identify and resolve issues.

Getting started with Python ORMs in FastAPI

To start using an ORM with FastAPI, follow these general steps:

Install the desired ORM (e.g., sqlalchemy or tortoise) using pip. Create a database connection using your chosen ORM. Define your application's data models as classes or structs. Use the ORM to create tables and perform CRUD (Create, Read, Update, Delete) operations on those tables. Integrate your ORM with FastAPI routes, allowing you to expose data to API consumers.

Remember, the specific steps and configuration details will vary depending on the ORM and database system you choose. For more information, I recommend exploring the official documentation for each ORM and FastAPI's tutorials on integrating ORMs.