Library API

A FastAPI-based REST service to manage library books using in-memory storage.

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Setup Instructions

Prerequisites

Ensure you have Python 3.9+ installed on your system.

Create Virtual Environement

python -m venv venv
# In cmd.exe
venv\Scripts\activate.bat
# In PowerShell
venv\Scripts\Activate.ps1

Installation & Running the API

pip install -r requirement.txt
uvicorn main:app --reload

The API will start at http://127.0.0.1:8000

Access the Swagger UI

Open your browser and visit:

http://127.0.0.1:8000/docs

This interactive Swagger UI allows you to test all API endpoints directly.

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The Logic (How I Thought)

Why FastAPI?

1. High Performance: FastAPI is built on Starlette and async Python, delivering excellent performance for REST APIs
2. Automatic API Documentation: Swagger UI is auto-generated without extra effort
3. Built-in Validation: Pydantic models handle request/response validation automatically
4. Type Hints: Python type hints make code readable and enable IDE support
5. Industry Standard: FastAPI is production-ready and widely adopted for backend APIs

Why In-Memory Dictionary?

1. Simplicity: Python dictionaries provide O(1) lookup by ID, perfect for this use case
2. No External Dependencies: No need for databases or file systems
3. Fast Operations: All CRUD operations are instantaneous in-memory
4. Clear Implementation: Makes code logic transparent and easy to understand
5. Perfect for Prototyping: Ideal for demonstrating API functionality

How API Routes are Structured

The API is organized around RESTful principles:

• POST /books - Creates a new book resource
• GET /books/{id} - Retrieves a specific book by ID (unique identifier)
• GET /books/search?year=2024 - Queries books by publication year
• DELETE /books/{id} - Removes a book from inventory

Each endpoint:

• Validates input using Pydantic models
• Returns appropriate HTTP status codes (201 Created, 404 Not Found, 400 Bad Request)
• Includes clear error messages for invalid operations
• Uses in-memory dictionary as the data store

Hardest Bug Faced

Issue: Duplicate ID Handling

• Problem: Initially, adding a book with a duplicate ID would silently overwrite the existing book, causing data loss
• Root Cause: Missing validation check before insertion into the dictionary
• Solution: Added explicit if book_id in books_db check that raises an HTTPException with status code 400 (Bad Request). This ensures the API rejects duplicate IDs gracefully with a clear error message
• Learning: Always validate unique constraints before mutating the data store

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Output Screenshots

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Future Improvements

1. Persistent Database Integration

   • Replace in-memory storage with PostgreSQL/MongoDB for data persistence across sessions
   • Implement SQLAlchemy ORM for better database abstraction

2. Authentication & Authorization

   • Add JWT token-based authentication for secure API access
   • Implement role-based access control (admin, user, guest roles)
   • Protect endpoints with login requirements

3. Update Book Endpoint

   • Add PUT /books/{id} to update existing book details
   • Support partial updates with PATCH /books/{id}
   • Validate changes before updating

4. Pagination & Advanced Filtering

   • Implement offset/limit pagination for large book lists
   • Add filters by author, title, or year range
   • Support sorting by different fields

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How to Test

1. Start the API: uvicorn main:app --reload
2. Open Swagger UI: http://127.0.0.1:8000/docs
3. Use the interactive interface to test each endpoint:
   • Add books with different IDs
   • Retrieve books by ID
   • Search books by publication year
   • Delete books
4. All responses are displayed with their status codes and JSON format