YouTube Clone

Project Overview

Background

This project is a scalable video processing service that mimics YouTube's core functionality. Built with a modern cloud-native architecture, it leverages Google Cloud Platform and Firebase services to handle video processing, user authentication, and content delivery. The system demonstrates the implementation of serverless architecture and asynchronous video processing at scale.

Key Features

Secure Video Upload System: Implemented secure, signed URL generation for direct-to-cloud video uploads, with automatic processing triggers and status tracking.
Cloud-Native Video Processing: Built a robust video processing pipeline using FFmpeg for video conversion, with automatic scaling through Cloud Run and Pub/Sub for handling multiple concurrent uploads.
User Authentication & Management: Integrated Firebase Authentication with automatic user profile creation in Firestore, including secure session management and profile updates.

Technical Implementation

Architecture

The application uses a microservices architecture with several key components:

Frontend: Next.js application providing the user interface and handling client-side operations
Authentication: Firebase Auth with custom user creation triggers
Storage: Google Cloud Storage for raw and processed videos
Processing: Cloud Run container running Express.js for video processing
Database: Firestore for user data and video metadata
Message Queue: Cloud Pub/Sub for processing job coordination

Challenges & Solutions

Secure Video Upload

Challenge: Implementing secure, direct-to-cloud video uploads.
Solution: Developed a signed URL system using Google Cloud Storage, allowing temporary, secure upload URLs with automatic expiration.

Scalable Processing

Challenge: Handling multiple video processing requests efficiently.
Solution: Implemented a queue-based system using Cloud Pub/Sub and Cloud Run, allowing automatic scaling based on demand.

State Management

Challenge: Tracking video processing status across multiple services.
Solution: Used Firestore for real-time status updates and Firebase Cloud Functions for state transitions.

Key Learnings

This project provided deep insights into cloud-native architecture, particularly in handling large media files and implementing serverless processing pipelines. Key takeaways include effective use of cloud services for scalability, implementing secure file handling, and managing asynchronous processes in a distributed system.