Dabas 1.0.2

A Python library for Database Management

🚀 Dabas: Lightweight Database Management Library - V1.0.2

🛠️ Version 1.0.2

🌟 Introduction

Dabas is a lightweight, easy-to-use library built on top of SQLAlchemy to simplify database operations in Python.

It provides a streamlined interface for connecting to databases, managing sessions, and performing CRUD operations with minimal effort.

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✨ Features

• 🔁 Automatic Transaction Management – Ensures safe commits and rollbacks.
• 🛠️ Session Handling – Provides a clean API for managing database sessions.
• 🔗 Flexibility – Supports multiple database engines via SQLAlchemy.
• ⚡ Lightweight & Efficient – Designed to be minimal while offering essential functionality.
• 🔍 Advanced Filtering – Supports OR/AND/range conditions.
• 📥 Data Insertion – Insert and bulk insert support.
• ✏️ Data Modification – Update and bulk update capabilities.
• 📄 Easy Pagination – Simplifies data navigation.
• 🛡️ Safe Deletion – Protects data with rollback support.
• 📦 Consistent Output Handling – Ensures structured data response.

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📚 Requirements

• Python 3.7+
• SQLAlchemy >= 1.4

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🔧 Installation

Install Dabas via pip:

pip install Dabas

💡 Quick Start

Here’s how you can quickly set up and use Dabas in your project.

from Dabas import DatabaseManager, EngineFactory
from sqlalchemy import Column, Integer, Float, String
from sqlalchemy.orm import declarative_base
from time import time

Base = declarative_base()

class Order(Base):
    __tablename__ = "orders"

    order_id = Column(Integer, primary_key=True)
    product = Column(String, nullable=False)
    price = Column(Float, nullable=False)
    time = Column(Integer, nullable=False)

    def __init__(self, order_id, product, price, time):
        self.order_id = order_id
        self.product = product
        self.price = price
        self.time = time

# Example data
order_1 = {"order_id": 1, "product": "product_1", "price": 100, "time": time()}
order_2 = Order(order_id=2, product="product_2", price=200, time=time())

# Database setup
engine = EngineFactory("data.db").sqlite()
db = DatabaseManager(engine=engine, base=Base)

# Create tables if they don't exist
db.create_tables()

# Insert records
db.insert(Order(**order_1))
db.insert(order_2)

# Query data
orders = db.get(Order, limit=2).to_json()
print(orders)

🖥️ Expected Output

[
    {
        "order_id": 1,
        "price": 100.0,
        "product": "product_1",
        "time": 1746916053.5904622
    },
    {
        "order_id": 2,
        "price": 200.0,
        "product": "product_2",
        "time": 1746916053.5904622
    }
]

Advanced Examples with Dabas

1️⃣ Bulk Insert Data Efficiently

# Insert multiple orders in one transaction
bulk_orders = [
    {"order_id": 3, "product": "product_3", "price": 150, "time": time()},
    {"order_id": 4, "product": "product_4", "price": 250, "time": time()},
    {"order_id": 5, "product": "product_5", "price": 350, "time": time()},
]

db.bulk_insert([Order(**order) for order in bulk_orders])

✅ Faster insertion

✅ Minimizes database overhead

2️⃣ Query with Filters (OR, AND, Range)

# Get orders where price is between 100 and 300
filtered_orders =  db.search(model_class,  range_filters={"price": (100, 200)}).to_json()



# Get orders with specific conditions (OR)
or_filtered_orders =db.search(model_class, or_conditions=[("product", "product_1"), ("price", 250)]).to_json()


# Get orders with specific conditions (AND)
and_filtered_orders =db.search(model_class, and_conditions=[("product", "product_4"), ("price", 250)]).to_json()


print(filtered_orders, or_filtered_orders, and_filtered_orders)

✅ Flexible filtering with OR/AND and range condition

3️⃣ Update Records with Bulk Updat

# Update multiple records at once
update_data = [{"order_id": 3, "product": "Updated_Product_3"}, {"order_id": 4, "price": 275}]
db.bulk_update(Order, update_data)

✅ Easily update multiple records in one operation

4️⃣ Safe Deletion with Rollback Suppor

# Delete an order safely
db.delete(Order, filters={"order_id":5})

✅ Ensures rollback support in case of errors

5️⃣ Pagination for Large Dataset

# Get paginated results (2 items per page)
page_1 = db.paginate(page=1, per_page=2).to_json()
page_2 = db.paginate(page=2, per_page=2).to_json()

print(page_1, page_2)

✅ Easier navigation in large datasets

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🎯 Summary of New Features

✅ Bulk insert for efficient data handling

✅ Advanced filtering with OR/AND/Range conditions

✅ Bulk updates for multiple records at once

✅ Safe deletion with rollback protection

✅ Pagination for large queries

📖 Documentation

For more details, visit the official SQLAlchemy documentation.

📜 License

This project is licensed under the MIT License.

💖 Sponsor

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