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SpongeBob Episodes Explorer

This project is a React application that demonstrates efficient handling of large datasets with custom filtering and virtualized rendering. It showcases SpongeBob SquarePants episodes with advanced filtering capabilities and optimized performance.

Key Features

  1. Large Dataset Handling: Efficiently manages a large dataset of SpongeBob episodes.

  2. Advanced Filtering:

    • Title search
    • Multi-select filters for seasons, writers, copyright years, and characters
    • Real-time filtering with debounced updates

  3. Virtualized Rendering:

    • Uses react-window for efficient rendering of large lists
    • Implements infinite scrolling with react-window-infinite-loader

  4. Performance Optimizations:

    • Memoized components and callbacks
    • Debounced filter updates
    • Lazy loading of episode cards

  5. Responsive UI:

    • Built with Chakra UI for a clean, responsive design
    • Grid layout for filter sidebar and main content

  6. Loading States:

    • Skeleton loaders for episode cards during filtering
    • Display of filtering time and episode count

Technical Stack

  • React
  • TypeScript
  • Chakra UI
  • react-window & react-window-infinite-loader for virtualization
  • react-virtualized-auto-sizer for responsive sizing

Getting Started

  1. Clone the repository
  2. Run npm install to install dependencies
  3. Run npm start to start the development server
  4. Open http://localhost:3000 to view it in the browser

Performance Considerations

  • The app uses memoization and virtualization to handle large datasets efficiently
  • Filtering is optimized with debounced updates to reduce unnecessary re-renders
  • Lazy loading is implemented for episode cards to improve initial load time

Future Improvements

  • Implement server-side filtering for even larger datasets
  • Add sorting functionality
  • Enhance accessibility features
  • Implement unit and integration tests

This project demonstrates best practices for handling large datasets in React applications, showcasing efficient filtering, virtualization, and performance optimization techniques.

Contact

For any questions or feedback regarding this project, please contact:

Email: aliftan29@gmail.com