What if your Python database code didn't care which database you used?
Introduction Writing raw SQL queries across PostgreSQL, SQLite, MySQL, SQL Server, and Oracle usually means wrangling five different drivers and endless setup boilerplate. Object-Relational Mappers (ORMs) like SQLAlchemy solve driver fragmentation, but they introduce heavy performance overhead, complex abstractions, and a steep learning curve when all you want to do is execute straightforward…
Writing raw SQL queries across various databases often involves managing multiple drivers and boilerplate setup. Object-Relational Mappers (ORMs) like SQLAlchemy can simplify this, but they come with performance overhead and a steep learning curve. SQLPyHelper offers a lightweight, unified API for Python applications, bypassing ORM bloat.
It provides a clean, parameterized SQL execution with built-in connection pooling and multi-database compatibility. The API supports five major databases simultaneously, including SQLite, PostgreSQL, MySQL, SQL Server, and Oracle. It also includes native async support for FastAPI or asyncio, built-in migration and CSV exports, zero-config environments that automatically load credentials from a .env file, and production-ready safety features such as connection pooling, automatic reconnection handling, explicit transaction management, and SQL injection protection through parameterization.
The API is designed to work seamlessly for both synchronous scripts and asynchronous web services. To use SQLPyHelper, you can start with a synchronous context manager or an async helper, depending on your application's needs. Installation is simple and modular, allowing you to install only the specific database drivers your application requires.
The project is open-source, and you can contribute, report issues, or check out the documentation on GitHub.
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