Skip to content

Latest commit

 

History

History
155 lines (132 loc) · 3.69 KB

challenge.md

File metadata and controls

155 lines (132 loc) · 3.69 KB

Movie Watchlist Application

Time Frame: 48 hours Tech Stack: Choose your preferred:

  • Frontend: React/Vue/Angular
  • Backend: Node.js/Python/Java
  • Database: Any database of your choice

Project Overview

Create a web application that allows users to manage their movie watchlist.

Core Requirements

Frontend Requirements

  1. Movie List View

    • Grid/list view of movies with pagination
    • Each movie displays:
      • Title
      • Release year
      • Status (watched/unwatched)
      • Rating (if watched)
      • Genre(s)
    • Basic filtering:
      • By genre
      • By watch status
      • By rating
    • Simple search by title

  2. Movie Details View

    • Display movie information
    • Mark as watched/unwatched
    • Rate movie (1-5 stars)

  3. Add Movie Form

    • Title (required)
    • Release Year (required)
    • Genre(s) (multiple selection)

  4. UI Requirements

    • Responsive design
    • Loading states
    • Basic error handling
    • Form validation

Backend Requirements

  1. API Endpoints

    /api/v1/
├── /movies
│   ├── GET / (list, pagination, filters)
│   ├── GET /:id (details)
│   ├── POST / (create)
│   ├── PUT /:id (update)
│   └── DELETE /:id (remove)
└── /genres
    └── GET / (list all genres)

  2. Data Models

    Movie {
  id: uuid
  title: string
  releaseYear: number
  genres: string[]
  watched: boolean
  rating: number (1-5)?
  createdAt: timestamp
  updatedAt: timestamp
}

Genre {
  id: uuid
  name: string
}

Optional Challenge: Movie Recommendations

Implement a recommendation system with the following features:

  1. Algorithm Requirements

    • When viewing a movie, recommend similar movies based on:
      • Genre overlap
      • Rating patterns
      • Release year proximity
    • Add a new endpoint: GET /api/v1/recommendations/:movieId

  2. Performance Requirements

    • Recommendations should return in under 200ms
    • Support for large datasets (10,000+ movies)
    • Implement appropriate caching

  3. Implementation Considerations

    • Document your algorithm design
    • Explain performance optimizations
    • Consider memory vs. computation trade-offs

Additional Optional Features

  1. Technical Enhancements

    • Unit tests
    • Docker setup
    • API documentation
    • Performance monitoring
    • Advanced filtering and sorting
    • Infinite scroll
    • Authentication

  2. UI Enhancements

    • Dark/light mode
    • Analytics dashboard
    • Bulk actions
    • Advanced search

Evaluation Criteria

  1. Code Quality (40%)

    • Clean, maintainable code
    • Modern best practices
    • Error handling
    • Code organization

  2. Functionality (30%)

    • Feature completeness
    • Performance
    • Edge case handling

  3. Technical Decisions (20%)

    • Architecture choices
    • Technology stack decisions
    • Code patterns
    • Data persistence strategy

  4. Documentation (10%)

    • Clear README
    • Setup instructions

Submission Requirements

  1. GitHub Repository

    • Source code
    • Documentation (optional)
    • Tests (optional)
    • Sample data seed script

  2. README.md including:

    • Setup instructions (different from the docs)
    • Technology choices and rationale (including database choice)

  3. Working Demo:

    • Local setup instructions
    • Sample dataset

Notes

  • Focus on completing core requirements with high quality
  • Choose technologies and persistence solutions that you're comfortable with
  • Optional features and challenges should only be attempted after core features are solid
  • Document your technical decisions and their rationale
  • Handle errors gracefully