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Introduction

The hls-microservice-backend application is designed to transform video files into HLS (HTTP Live Streaming) format using a microservices architecture. This document provides an overview of the application's components, technologies used, and how they interact.

Architecture Overview

The application follows a microservices architecture, which allows for scalability and efficient management of different components. Below is a basic diagram illustrating the architecture and data flow:

Key Components

  1. Express Server:

    • Handles HTTP requests for video uploads and fetching processed videos.
    • Interacts with MongoDB to store and retrieve video metadata.
    • Publishes messages to RabbitMQ to queue video processing tasks.

  2. Video Processing Service:

    • Listens to RabbitMQ for video processing tasks.
    • Uses ffmpeg to convert videos to HLS format.
    • Updates the status of videos in MongoDB after processing.

  3. MongoDB:

    • Stores metadata about videos, including their status and file paths.

  4. RabbitMQ:

    • Manages the queue for video processing tasks.
    • Ensures that video processing tasks are handled asynchronously.

Technologies Used

  • Node.js: JavaScript runtime for building the server and processing services.
  • TypeScript: Superset of JavaScript that adds static types.
  • Express: Web framework for Node.js to handle HTTP requests.
  • MongoDB: NoSQL database for storing video metadata.
  • RabbitMQ: Message broker for managing video processing tasks.
  • ffmpeg: Command-line tool for processing video and audio files.
  • Docker: Container platform for managing software processes in isolated environments.
  • Kubernetes: Orchestration system for automating deployment, scaling, and management of containerized applications.
  • Helm: Kubernetes package manager for deploying and managing applications.

Data Flow

  1. Video Upload:

    • A user uploads a video via an HTTP request to the Express server.
    • The server saves the video file and stores metadata in MongoDB.
    • The server publishes a message to RabbitMQ to queue the video for processing.

  2. Video Processing:

    • The Video Processing Service listens to RabbitMQ for new video processing tasks.
    • Upon receiving a task, it retrieves the video file and uses ffmpeg to convert it to HLS format.
    • After processing, it updates the video's status in MongoDB.

  3. Fetching Processed Videos:

    • Users can fetch the processed videos via HTTP requests to the Express server.
    • The server retrieves the video metadata from MongoDB and serves the processed video files.

Conclusion

This document provides an overview of the microservice-example_ application, including its architecture, key components, technologies used, and data flow. For more detailed instructions on deploying the application using Helm, please refer to the README.md file.