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AI-Based Cloud Database Management System

🚀 An AI-powered database management system that optimizes query performance and safeguards data integrity using Flask, SQLAlchemy, and Scikit-learn.

Image

📖 Description

The AI-Based Cloud Database Management System is a Flask-powered API that allows users to manage simulated cloud database transactions while utilizing a basic AI model to suggest query optimizations. The project enables storing, retrieving, and analyzing database transactions while providing automated AI-based recommendations to improve performance.

This system simulates a real-world cloud database where AI-driven analytics optimize query execution times based on historical transaction data.

⭐ Features

✅ Flask REST API for handling database transactions.
✅ SQLite Integration to store and retrieve transactions.
✅ Basic AI Module to analyze and suggest query optimizations.
✅ Automated Transaction Simulation for testing system behavior.
✅ Postman & cURL Support for easy API interaction.
✅ Error Handling & Debugging to prevent unexpected crashes.

🛠 Tech Stack & Why

Technology Purpose
Flask Lightweight web framework for API development.
Flask-SQLAlchemy ORM for managing database interactions.
SQLite Simple and efficient local database.
Scikit-learn AI module for performance optimization.
pandas Data handling and preprocessing.
python-dotenv Securely managing environment variables.
  • Flask is easy to use and lightweight, perfect for building RESTful APIs.
  • SQLAlchemy simplifies database operations without complex SQL queries.
  • SQLite provides a lightweight, embedded database for local testing.
  • Scikit-learn enables simple AI-based performance tuning using regression models.

🏗 Installation & Setup

🔹 1. Clone the Repository

git clone https://github.com/Vaskar71/AI-Driven-Cloud-Database-Performance-Optimizer.git
cd AI-Cloud-DB-Management

🔹 2. Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # On macOS/Linux
venv\Scripts\activate     # On Windows

🔹 3. Install Dependencies

pip install -r requirements.txt

🔹 4. Set Up Environment Variables

Create a .env file and add the following configuration:

FLASK_APP=app.py
FLASK_DEBUG=1
DATABASE_URL=sqlite:///cloud_db.sqlite3
SECRET_KEY=your_secret_key

🔹 5. Initialize the Database

python -c "from app import db; db.create_all()"

🔹 6. Run the Flask Application

python app.py

🚀 Usage

1️⃣ Check if the API is Running

Visit http://127.0.0.1:5000/ in your browser. You should see:

Welcome to the AI-Based Cloud Database Management System!

2️⃣ Adding a New Transaction (POST Request)

Use Postman or cURL to send a request:

curl -X POST http://127.0.0.1:5000/transaction -H "Content-Type: application/json" \
-d '{"operation":"INSERT","data":"Sample data","performance_metric":2.0}'

3️⃣ Retrieving All Transactions (GET Request)

curl http://127.0.0.1:5000/transactions

4️⃣ Run the AI Optimization Simulation

python simulation.py

⚙️ Configuration

Environment Variable Description
FLASK_APP Defines the Flask entry point.
FLASK_DEBUG Enables debugging mode.
DATABASE_URL Sets the database connection URL.
SECRET_KEY Used for session security.

To modify AI behavior, adjust the training dataset inside ai_optimizer.py.

🔥 API Endpoints

Method Endpoint Description
GET / Welcome message.
POST /transaction Adds a new database transaction.
GET /transactions Retrieves all stored transactions.

Example Request (POST /transaction):

{
  "operation": "INSERT",
  "data": "Sample entry",
  "performance_metric": 2.5
}

Example Response (201 Created):

{
  "message": "Transaction added successfully.",
  "id": 1
}

🤖 AI Optimization Module

  • Uses Linear Regression (Scikit-learn) to predict the best database optimization level based on performance metrics.
  • Called automatically after every transaction to suggest improvements.
  • Can be tested manually by running: 
    python ai_optimizer.py

Expected Output:

For a performance metric of 1.8, Suggested optimization level: 3.10

📸 Screenshots

📌 [Attach relevant screenshots here]

  • ✅ Flask API running (python app.py) Image Image
  • ✅ Postman API test (POST /transaction) Image
  • ✅ AI Optimization Output (python simulation.py) Image
  • ✅ Database Structure in SQLite
    Image

🤝 Contributing

We welcome contributions! To contribute:

  1. Fork this repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Commit your changes (git commit -m "Added new feature").
  4. Push the branch (git push origin feature-branch).
  5. Create a Pull Request.

📩 Contact

For issues and feedback, create a GitHub Issue or contact:
📧 Email: [email protected]