jays-tools 0.1.8

A collection of reusable utilities across projects I work on.

Jay Tools

This package is a collection of tools that I tend to find useful in my projects. It is not meant to be a comprehensive library, but rather a collection of utilities that I find useful.

Installation

You can install the package via pip:

pip install jays-tools

Tools

JsonDatabase

A lightweight JSON-backed database with Pydantic validation and type hints. Perfect for small projects, embedded data stores, or prototypes where you want structured data without the complexity of traditional databases.

Inspiration

I love tools like SQL, Redis, and other databases for live production data where multiple users are interacting with the same data simultaneously. However, my frustrations were the typing overhead, writing exhaustive tests just to validate schemas, and the complexity that comes with it when all I need is a simple way to store data in a project.

SQLAlchemy is powerful but can get heavy and overwhelming fast. SQLModel is a step in the right direction, but it has its rough edges — some features require workarounds that feel more like hacks than solutions.

The database I enjoyed most was TinyDB. It lacked typing support, but the concept and API were exactly what I wanted — especially for projects like Local osu! Server, where only a single user is ever interacting with the data.

So I built this: a simple JSON database with the full benefits of Pydantic models, type hints, and surprisingly painless migrations — all without the overhead of a traditional database setup.

Usage

from jays_tools.json_database import JsonDatabase
from pydantic import BaseModel

class User(BaseModel):
    id: int
    name: str

class Users(BaseModel):
    total: int = 0
    users: list[User] = []

# Create or load database. auto_init=True creates file if it doesn't exist
db = JsonDatabase("users.json", Users, auto_init=True)

# Sync usage - acquires lock, reads, modifies, writes on exit
with db as users_data:
    users_data.users.append(User(id=1, name="Jay"))
    users_data.users.append(User(id=2, name="John"))
    users_data.total = len(users_data.users)
    # Automatically writes on context exit

# Async usage - same pattern, non-blocking I/O via thread pool
async with db as users_data:
    if users_data is not None:
        users_data.users.append(User(id=3, name="Jane"))
        users_data.total = len(users_data.users)
        # Automatically writes on context exit