Django Task Project - README

Overview

This project demonstrates a robust implementation of Django and Python development with a focus on APIs, custom authentication, and task scheduling. It integrates Microsoft SQL Server (Local) as the database backend and utilizes Celery for asynchronous and scheduled tasks. The project is containerized using Docker for seamless deployment, except for the SQL Server, which needs to be set up locally.

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Features

1. Project Setup

• Developed using Django with Python 3.9.
• Uses Microsoft SQL Server (Local) as the database backend.

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2. Custom Authentication and Authorization

• Implements JWT (JSON Web Tokens) for secure user authentication.
• Tokens are set to expire after 5 minutes for enhanced security.
• Protects all endpoints to ensure only authenticated users can access them.

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3. API Endpoints

a. Post API

• Allows authenticated users to create tasks by providing a title and duration.
• Automatically stores timestamps and associates tasks with the logged-in user.

b. Get API

• Retrieves the last 4 tasks created by the logged-in user using Django's filtering capabilities.

c. Retrieve API

• Fetches a specific task created by the logged-in user.
• Uses a raw SQL query for implementation.

d. Update API

• Allows updating only the title of a task.
• Prevents updates to the duration field.
• Implemented using a raw SQL query.

e. Delete API

• Enables users to delete tasks they created.
• Prevents deletion of tasks created by other users.

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4. Test Cases

• Includes comprehensive test cases to validate all API endpoints and custom functionalities.

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5. Custom Management Command

• Implements a Django management command to:
  • Iterate through all tasks in the database.
  • Display each task at intervals of 10 seconds (using custom output instead of print()).

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6. Celery Integration

• Integrated Celery with Redis to manage asynchronous task execution.
• Handles background task processing efficiently.

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7. Scheduled Tasks

• Configured Celery Beat to run scheduled tasks:
  • A scheduled task runs every 1 minute, printing the title, duration, and timestamps of tasks created by the user with id=1.

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8. Docker Containerization

• Fully containerized project with Docker for deploying the Django application and Celery services.
• The Dockerfile and docker-compose.yaml configure the following services:
  • Django App: The main web application.
  • Redis: A message broker for Celery.
  • Celery Worker: Executes background tasks.
  • Celery Beat: Manages task scheduling.

Note: The SQL Server is not included in the Docker setup and must be installed and configured locally.

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Setup Instructions

Prerequisites

• Docker and Docker Compose.
• Microsoft SQL Server installed locally.
• Django configured to connect to the local SQL Server.

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Running the Project

Step 1: Clone the Repository

git clone <repo_url>
cd <repo_directory>

Step 2: Configure SQL Server

• Install Microsoft SQL Server locally.
• Update the DATABASES setting in settings.py to match your local SQL Server configuration.

Example:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.sql_server',
        'NAME': '<database_name>',
        'USER': '<username>',
        'PASSWORD': '<password>',
        'HOST': 'localhost',
        'PORT': '1433',
    }
}

Step 3: Build and Start Docker Services

• Start the Django app, Redis, and Celery services using Docker:

docker-compose up --build

Step 4: Run Database Migrations

• Apply migrations to the SQL Server:

docker exec -it <container_name> python manage.py migrate

Step 5: Access the Application

• The Django app will be available at http://localhost:8000.

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Technologies Used

• Django: Web application framework.
• Python 3.9: Programming language.
• Microsoft SQL Server: Local database backend.
• JWT: Authentication and authorization.
• Celery: Asynchronous task processing.
• Redis: Message broker for Celery.
• Celery Beat: Scheduled task management.
• Docker: Application containerization.

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Key Notes

• SQL Server is configured locally and is not included as part of the Docker containers.
• Celery and Celery Beat are automatically started within the Docker containers.
• Scheduled tasks run periodically without any additional manual configuration.
• Test the APIs using tools like Postman for validation.