Library Management System

A RESTful web application for managing a library, built with Python/Flask and SQLite. This project is designed for performance testing with JMeter.

Project Overview

This library system allows users to:

• Search and browse books
• Filter by author, year, and genre
• Checkout and return books
• View personalized recommendations based on checkout history
• See trending books (most checked out in the last 7 days)

Tech Stack

• Backend: Python 3.11 + Flask
• Database: SQLite
• Frontend: HTML, CSS, JavaScript (vanilla)
• Container: Docker

Project Structure

library-project/
├── app/
│   ├── __init__.py          # Flask app factory
│   ├── database.py          # Database schema and connection
│   └── routes/
│       ├── __init__.py
│       ├── users.py         # User CRUD routes
│       ├── books.py         # Book routes with search/filter
│       ├── checkouts.py     # Checkout/return routes
│       └── homepage.py      # Homepage with recommendations
├── templates/
│   └── index.html           # Main HTML template
├── static/
│   ├── css/style.css        # Styles
│   ├── js/app.js            # Frontend JavaScript
│   └── images/              # Static images
├── scripts/
│   └── import_data.py       # Data import script
├── data/                    # Database and CSV files
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── run.py                   # Application entry point

REST API Routes

Method	Route	Description
GET	/api/books	List books with filters
GET	/api/books/	Get single book
GET	/api/books/search?q=	Search books
GET	/api/books/filters	Get filter options
PATCH	/api/books/	Update book availability
OPTIONS	/api/books	Get allowed methods
POST	/api/users	Register user
POST	/api/users/login	Login user
GET	/api/users/	Get user
PUT	/api/users/	Update user
DELETE	/api/users/	Delete user
POST	/api/checkouts	Checkout a book
GET	/api/checkouts	List checkouts
GET	/api/checkouts/	Get checkout details
DELETE	/api/checkouts/	Return a book
GET	/api/homepage	Get homepage data
GET	/api/homepage/trending	Get trending books
GET	/api/homepage/recommendations/	Get user recommendations

Setup Instructions

1. Download the Dataset

Download the Book Recommendation Dataset from Kaggle: https://www.kaggle.com/datasets/arashnic/book-recommendation-dataset

Place Books.csv in the data/ directory.

2. Run with Docker (Recommended - Windows)

run.bat

This script will:

• Build Docker images

• Start all required containers using docker-compose

• Launch the application automatically

After startup, access the application at:

http://localhost:5000/

Requirements:

• Docker Desktop installed and running

• Windows environment (for .bat script)

3. Run Locally (Alternative)

# Install dependencies
pip install -r requirements.txt

# Run the application
python run.py

Performance Testing Notes

This application includes intentional inefficiencies for performance testing:

1. No Caching: Recommendations are recalculated on every page load
2. Multiple Queries: Separate database queries for each recommendation type
3. N+1 Query Pattern: Similar users recommendations use nested loops
4. Full Table Scans: Search uses LIKE queries without optimized indexes
5. Image Loading: External images loaded on every request

Areas to Optimize

After baseline JMeter testing, consider optimizing:

• Add caching for trending books and recommendations
• Combine recommendation queries into single optimized query
• Add database indexes for frequent search patterns
• Implement lazy loading for images
• Add pagination to recommendations
• Use connection pooling

Test Users

After running import_data.py, sample users are created:

• Username: user1 to user100
• Password: pass1 to pass100

Example: Login with user1 / pass1

JMeter Testing

Key endpoints to test:

1. GET /api/homepage?user_id=1 - Full homepage with recommendations
2. GET /api/books?search=python - Book search
3. POST /api/checkouts - Book checkout
4. GET /api/homepage/recommendations/1 - Recommendations only

Team Members

• [Brian Nguyen]
• [Owen Davis]
• [Dilraj Sooch]
• [Kahlib Stewart]

Documentation

This project uses Doxygen to generate API documentation from Python docstrings.

Generate Documentation with Docker

# Using Make (recommended)
make docs

# Or using docker-compose directly
docker-compose run --rm docs

Generate and Serve Documentation

# Using Make - serves on http://localhost:8080
make docs-serve

# Or manually
docker-compose run --rm docs
docker-compose --profile docs-server up -d docs-server

Generate Documentation Locally

If you have Doxygen installed locally:

# Install Doxygen (Ubuntu/Debian)
sudo apt-get install doxygen graphviz

# Generate documentation
doxygen Doxyfile
# Or use Make
make docs-local

View Documentation

After generation, open docs/html/index.html in your browser, or access http://localhost:8080 if using the docs-server.

Documentation Structure

• Doxyfile - Doxygen configuration file
• docs/html/ - Generated HTML documentation (gitignored)
• docs/doxygen_warnings.log - Build warnings log

Course

CSCN73060 - Web Project