File-based ORM for dataclasses.
Project description
Datafiles: A file-based ORM for dataclasses
Datafiles is a bidirectional serialization library for Python dataclasses to synchronizes objects to the filesystem using type annotations. It supports a variety of file formats with round-trip preservation of formatting and comments, where possible. Object changes are automatically saved to disk and only include the minimum data needed to restore each object.
Popular use cases include:
- Coercing user-editable files into the proper Python types
- Storing program configuration and data in version control
- Loading data fixtures for demonstration or testing purposes
- Prototyping data models agnostic of persistence backends
Watch my lightning talk for a demo of this in action!
Overview
Take an existing dataclass such as this example from the documentation:
from dataclasses import dataclass
@dataclass
class InventoryItem:
"""Class for keeping track of an item in inventory."""
name: str
unit_price: float
quantity_on_hand: int = 0
def total_cost(self) -> float:
return self.unit_price * self.quantity_on_hand
and decorate it with a directory pattern to synchronize instances:
from datafiles import datafile
@datafile("inventory/items/{self.name}.yml")
@dataclass
class InventoryItem:
...
Then, work with instances of the class as normal:
>>> item = InventoryItem("widget", 3)
# inventory/items/widget.yml
unit_price: 3.0
Changes to the object are automatically saved to the filesystem:
>>> item.quantity_on_hand += 100
# inventory/items/widget.yml
unit_price: 3.0
quantity_on_hand: 100
Changes to the filesystem are automatically reflected in the object:
# inventory/items/widget.yml
unit_price: 2.5 # <= manually changed from "3.0"
quantity_on_hand: 100
>>> item.unit_price
2.5
Objects can also be restored from the filesystem:
>>> from datafiles import Missing
>>> item = InventoryItem("widget", Missing)
>>> item.unit_price
2.5
>>> item.quantity_on_hand
100
Installation
Because datafiles relies on dataclasses and type annotations, Python 3.7+ is required. Install this library directly into an activated virtual environment:
$ pip install datafiles
or add it to your Poetry project:
$ poetry add datafiles
Documentation
To see additional synchronization and formatting options, please consult the full documentation.
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