ODBMS - Object Document/Relational Mapping System

A flexible and modern Python ORM supporting multiple databases (MongoDB, PostgreSQL, MySQL) with both synchronous and asynchronous operations.

Features

• Support for multiple databases:
  • SQLite (using sqlite3)
  • MongoDB (using Motor)
  • PostgreSQL (using aiopg)
  • MySQL (using aiomysql)
• Both synchronous and asynchronous operations
• Connection pooling for better performance
• Type-safe field definitions
• Pydantic integration for validation
• Automatic table/collection creation
• Relationship handling
• Computed fields
• Flexible query interface

Installation

pip install -r requirements.txt

Quick Start

from odbms import Model, StringField, IntegerField, EmailField
from odbms.dbms import DBMS

# Initialize database connection
DBMS.initialize(
    dbms='postgresql',  # or 'mongodb', 'mysql'
    host='localhost',
    port=5432,
    database='mydb',
    username='user',
    password='pass'
)

# Define your model
class User(Model):
    name: str = StringField()
    email: str = EmailField()
    age: int = IntegerField(min_value=0)

# Create a new user
user = User(name='John Doe', email='john@example.com', age=30)
await user.save_async()  # or user.save() for sync operation

# Find users
users = await User.find_async({'age': {'$gte': 25}})  # or User.find() for sync

Field Types

• StringField: For text data
• IntegerField: For integer values
• FloatField: For floating-point numbers
• BooleanField: For true/false values
• DateTimeField: For timestamps
• EmailField: For email addresses with validation
• IDField: For primary keys/IDs
• ComputedField: For dynamically computed values
• ListField: For arrays/lists
• DictField: For nested documents/objects

Database Operations

Synchronous Operations

# Create
user = User(name='John', email='john@example.com')
user.save()

# Read
user = User.find_one({'email': 'john@example.com'})
users = User.find({'age': {'$gte': 25}})
all_users = User.all()

# Update
User.update({'age': {'$lt': 18}}, {'is_minor': True})

# Delete
User.remove({'status': 'inactive'})

# Aggregation
total_age = User.sum('age', {'country': 'US'})

Asynchronous Operations

# Create
user = User(name='Jane', email='jane@example.com')
await user.save_async()

# Read
user = await User.find_one_async({'email': 'jane@example.com'})
users = await User.find_async({'age': {'$gte': 25}})
all_users = await User.all_async()

# Update
await User.update_async({'age': {'$lt': 18}}, {'is_minor': True})

# Delete
await User.remove_async({'status': 'inactive'})

# Aggregation
total_age = await User.sum_async('age', {'country': 'US'})

Relationships

class Post(Model):
    title: str = StringField()
    content: str = StringField()
    author_id: str = IDField()

class User(Model):
    name: str = StringField()
    posts: List[Post] = ListField(model=Post)

# Create related records
user = User(name='John')
await user.save_async()

post = Post(title='Hello', content='World', author_id=user.id)
await post.save_async()

# Access relationships
user_posts = await user.posts  # Automatically fetches related posts

Testing

Run the test suite:

pytest tests/

The test suite includes comprehensive tests for:

• All database operations (CRUD)
• Both sync and async operations
• Field validations
• Relationships
• Computed fields
• Aggregations

Requirements

• Python 3.7+
• pydantic >= 2.0.0
• motor >= 3.3.0 (for MongoDB)
• aiopg >= 1.4.0 (for PostgreSQL)
• aiomysql >= 0.2.0 (for MySQL)
• inflect >= 5.0.0
• python-dotenv >= 0.19.0

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License