sqla-lite 1.0.0

Lightweight and declarative wrapper over SQLAlchemy

sqla-lite

sqla-lite is a lightweight, declarative, and elegant wrapper over SQLAlchemy. It is designed to bring the joy and simplicity of frameworks like Java Spring Boot / JPA to Python, enabling you to define entities and repositories without the boilerplate of manually mapping declarative bases and session handlers.

Tired of using Mapped[int] = mapped_column(...) over and over? Welcome to sqla-lite.

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📦 Installation

This library relies strictly on SQLAlchemy >= 2.0.0 features. Ensure you have the dependencies installed:

pip install -r requirements.txt
# OR
pip install sqlalchemy>=2.0.0

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🚀 Quick Start (The Basics)

Define your models exactly like you would writing pure Python data structures. Use the @table class decorator instead of dealing with explicit inheritance from DeclarativeBase.

You can annotate your attributes by assigning the markers (Id(), Size()) directly!

from sqla_lite import table, Id, Size

@table("users")
class User:
    id: int = Id()             # Automatically setup as Primary Key
    name: str = Size(100)      # VARCHAR(100)
    age: int                   # Automatically inferred as Integer

Automatic Repositories

Say goodbye to the with Session(engine) as session: nightmare. With sqla-lite, you can register a repository to manage your Data Access layer globally for an entity!

from sqla_lite import repository, configure_database

# 1. Define an empty Repository pointing to your Entity
@repository(User)
class UserRepository:
    pass

# 2. Configure your Database globally ONCE (Usually at the start of your application)
from sqlalchemy import create_engine
engine = create_engine("sqlite:///:memory:")

# sqla-lite will generate all tables automatically in Base.metadata (if needed)
from sqla_lite.core import Base
Base.metadata.create_all(engine)

# Inform sqla-lite to use this global engine!
configure_database(engine)

# 3. Use it! No sessions needed!
repo = UserRepository()

user = User(name="John Doe", age=25)
repo.save(user) # Auto-commits

# Search by primary key natively!
john = repo.get(1)
print(john.name)

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⚡ Intermediate Usage

Type Inferences and Specific Mappings

sqla-lite understands your annotations and makes reasonable defaults.

• str automatically becomes String(256) if no Size() is provided.
• float becomes Float.
• Want highly-precise decimal numbers for currencies? Use the Decimal marker!

from sqla_lite import Decimal

@table("products")
class Product:
    id: int = Id()
    title: str = Size(150)
    
    # 10 digits in total, 2 fractional decimal numbers -> Numeric(10, 2)
    price: float = Decimal(precision=10, scale=2) 

Date Handling

Handling Date strings and casting them into Database Datetime correctly can be a headache. sqla-lite supports both:

1. Native Python formats: Using datetime.datetime directly.
2. String Parsing Formats: Use the DateFormat to transparently map python Strings into database DateTime seamlessly!

import datetime
from sqla_lite import DateFormat

@table("events")
class Event:
    id: int = Id()
    
    # Kept as native Datetime everywhere
    created_at: datetime.datetime 
    
    # Allows assigning strings in Python ("27/02/2026"). It'll be saved as a Datetime on DB!
    completed_at: str = DateFormat("%d/%m/%Y")

Example:

evt = Event(
    created_at=datetime.datetime.now(),
    completed_at="27/02/2026"
)
repo.save(evt)

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🌋 Advanced Usage

Composite Primary Keys

If your database design demands more complex structures like Many-To-Many resolution tables, or Legacy composite-keys, simply annotate multiple attributes with the Id() marker.

If one of the primary keys is a String and requires a size, you can pass the argument size= into the Id marker.

@table("employee_roles")
class EmployeeRole:
    # Key 1
    employee_id: int = Id()
    # Key 2: String with length!
    role_name: str = Id(size=50) 
    
    assigned_date: datetime.datetime

Querying with Repositories over Composite Keys

You don't need tuples or weird abstractions to retrieve composed key rows via our Repository Pattern. Just pass your identifiers in the sequence they were declared!

@repository(EmployeeRole)
class EmployeeRoleRepo: pass

repo = EmployeeRoleRepo()

# The Repository handles the argument unpacking dynamically
role = repo.get(101, "Software Engineer")
print(f"Loaded Role for Employee {role.employee_id}!")

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🔥 Extending Repositories

Because your Repository is a plain Python Class wrapped by @repository, you can implement custom behavior that fits your business logic inside of it. The decorator only injects basic (save, get, delete, find_all) methods, leaving you free to query anything else you like via self.engine:

from sqlalchemy.orm import Session

@repository(User)
class UserRepository:
    def find_adults(self):
        with Session(self.engine) as session:
            # self.entity_class holds a reference to the mapped Class!
            return session.query(self.entity_class).filter(self.entity_class.age >= 18).all()

# Usage:
repo = UserRepository()
adults = repo.find_adults()

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Created with ❤️. Say goodbye to boilerplate code!