russell310 / project_car

Car Search Web Application

This is a web application built with Django 4.2 and MySQL that allows users to search for cars based on various criteria (length, weight, velocity, color) and download the search results in XML format.

Features

• Search for cars using multiple criteria simultaneously.
• Download the search results as an XML file.
• Pagination support for search results.
• Fully tested to ensure software quality and execution.

Requirements

• Python 3.x
• Django 4.2
• MySQL

Installation

1. Clone the repository:

   git clone [e-mail]:russell310/project_car.git
   cd project_car

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

4. Configure the env:

   Copy .env.example with .env and add necessary settings for your environment

5. Apply migrations:

   python manage.py migrate

6. Load initial data:

   python manage.py populate_cars 100

7. Run the development server:

   python manage.py runserver

8. Access the application:

   Open your web browser and go to http://localhost:8000/.

Usage

• Search for Cars:

  • Use the search form to filter cars based on length, weight, velocity, and color.
  • Click the search button to view the results.

• Download Search Results:

  • Perform a search using the desired criteria.
  • Click the "Export to XML" button to download the search results as an XML file.

Testing

Run Tests

To run the tests, use the following command:

coverage run manage.py test
coverage report

This will run the tests and provide a coverage report.

Static Code Analysis

Flake8

Run Flake8 to check for code style issues:

flake8 car

Bandit

Run Bandit to check for security issues:

bandit -r car

Performance Testing with Locust

Run Locust to test load

locust -f car/locustfile.py --host=http://localhost:8000

Open Locust Web Interface

Open your web browser and go to http://localhost:8089.

Configure and Start the Load Test

• Set the Number of total users to simulate (e.g., 100).
• Set the Hatch rate (number of users to spawn per second, e.g., 10).
• Click the Start swarming button to start the load test.

Monitor the Test

Monitor the performance metrics in the Locust web interface, including requests per second, response times, and number of failures.

Author

Hamim Al Mahdi Russell

Contact

• Email: [e-mail]
• LinkedIn: linkedin.com/in/russell310
• GitHub: github.com/russell310
• Web: hamim.dev

For any further questions or contributions, please contact me via email or LinkedIn.