Ferro: The Pydantic x Rust ORM
Ferro is a high-performance, asynchronous ORM for Python, powered by a core engine written in Rust. Designed for simplicity and it only has one dependency: Pydantic. Delivering ergonomics familiar to the modern Pythonista and the speed and safety of Rust's SQLx and Sea-Query.
Key Features
- High-Performance Core: All SQL generation and row hydration are handled by a dedicated Rust engine, minimizing "Python Tax" on data-heavy operations.
- Async First: Built from the ground up for asynchronous applications, utilizing
pyo3-async-runtimesfor non-blocking I/O. - Pydantic Integration: Leverages Pydantic V2 for schema definition and data validation, providing full IDE support and type safety.
- Zero-Copy Intent: Designed with zero-copy principles to maximize throughput during large-scale data retrieval.
- Identity Map: Ensures object consistency across your application by tracking active model instances in a thread-safe registry.
Architecture
Ferro operates through a dual-layer architecture connected via a high-performance FFI (Foreign Function Interface) bridge:
- Python Layer: Developers define models using standard Python classes; a metaclass registers them with the backend.
- Rust Engine: Built on
SQLxandSea-Queryfor GIL-free row parsing and object instantiation.
Installation
Ferro is distributed as pre-compiled wheels for macOS, Linux, and Windows.
pip install ferro-orm
# Or with the migrations CLI (ferro migrate)
pip install "ferro-orm[cli]"
# Or with the Alembic bridge
pip install "ferro-orm[alembic]"
Schema changes go through auto-migrate while you develop and reviewed migrations (ferro migrate new, ferro migrate up) in production — see Schema Management. Ferro currently supports SQLite and PostgreSQL. Register multiple named connections with connect(..., name="...") when a process needs more than one database — see the connections guide.
Quick Start
import asyncio
from ferro import Field, Model, connect
class User(Model):
id: int | None = Field(default=None, primary_key=True)
username: str
is_active: bool = True
async def main():
await connect("sqlite:example.db?mode=rwc", auto_migrate=True)
# Create
alice = await User.create(username="alice")
# Query (lambda predicates — name the parameter after the model, e.g. user for User)
active_users = await User.where(lambda user: user.is_active == True).all()
print(f"Found {len(active_users)} active users.")
if __name__ == "__main__":
asyncio.run(main())
1 maintainer
Metadata
1 maintainer
Release files for ferro-orm 0.24.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| ferro_orm-0.24.0.tar.gz | 2.5 MB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| ferro_orm-0.24.0-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| ferro_orm-0.24.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| ferro_orm-0.24.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| ferro_orm-0.24.0-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
Total release size: 32.4 MB
Release files / ferro_orm-0.24.0.tar.gz
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