DSA Search Engine

A comprehensive search engine for data structures and algorithms, built with React, TypeScript, and Tailwind CSS.

📋 Features

• Search Engine: Find algorithms and data structures by name, category, or complexity
• Detailed Information: Access comprehensive details on time/space complexity, implementation, and use cases
• Examples: View practical examples of algorithm implementation and usage
• Responsive Design: Works seamlessly across desktop and mobile devices

🚀 Technologies Used

• React 19: The latest version of React with improved performance
• TypeScript: For static type checking
• Tailwind CSS: For utility-first styling
• Vite: For incredibly fast development and build
• React Router: For client-side routing
• Google OAuth: For authentication via Google

🛠️ Environment Setup

1. Clone this repository
2. Install dependencies with npm install
3. Create a .env.local file by copying .env.example
4. Add your Google OAuth credentials to .env.local (see GOOGLE_AUTH_SETUP.md for details)
5. Run the development server with npm run dev

Note: The .env.local file contains sensitive API keys and is ignored by Git. For deployment instructions to properly set up environment variables on hosting platforms, see DEPLOYMENT_ENV_SETUP.md.

🏗️ Project Structure

src/
├── components/       # Reusable UI components
├── pages/            # Route components
├── services/         # API and service interactions
├── hooks/            # Custom React hooks
├── utils/            # Helper functions
└── types/            # TypeScript type definitions

🛠️ Installation and Setup

1. Clone the repository

   git clone https://github.com/yourusername/dsa-search-engine.git
   cd dsa-search-engine

2. Install dependencies

   npm install

3. Start the development server

   npm run dev

4. Build for production

   npm run build

5. Deploy to GitHub Pages

   npm run deploy

   Or simply push to the main branch and GitHub Actions will deploy automatically.

📝 LeetCode Problem Updates

The website includes problems from LeetCode that are loaded from a static JSON file. This file is created by a Python scraper. There are multiple ways to update this data:

Manual Updates

Run the scraper script manually when you want to update:

cd backend
python leetcode_scraper.py

Automated Updates (Recommended)

The repository includes a GitHub Action that automatically updates the LeetCode problems once a month (on the 1st day at midnight UTC). This ensures users see up-to-date problems without manual intervention.

You can also trigger manual updates from the GitHub Actions tab in your repository.

Update Schedule

• GitHub Action runs automatically on the 1st day of each month
• Problems are scraped from LeetCode API
• Changes are committed automatically if new problems are found
• Vercel deployment updates with the new data

This means the website will stay up to date without you needing to manually run the scraper.

📚 Available Scripts

• npm run dev - Start the development server
• npm run build - Build for production
• npm run lint - Run ESLint to check code quality
• npm run preview - Preview the production build locally

🧠 Data Sources

Currently, the app uses mock data. In a production environment, this would be replaced with:

• A backend API serving algorithm data
• A database containing comprehensive algorithm information
• User-contributed examples and implementations

📝 Future Improvements

• Add authentication for user contributions
• Implement algorithm visualizations
• Add complexity comparisons between algorithms
• Create a community section for discussions
• Add a feature to save favorite algorithms

📄 License

MIT License

---

Made with ❤️ by DSA Search Engine Team