System Design for Online Streaming Service

High-Level System Design

  1. Architecture Overview:

    • Microservices Architecture: Each core functionality (user management, playback, recommendations) is a separate service for scalability.
    • Content Delivery Network (CDN): Ensures fast delivery of media to users with minimal latency.
    • Load Balancer: Distributes traffic across servers for high availability and scalability.
    • Database:
      • Relational DB (e.g., PostgreSQL): For user profiles, subscriptions, and payment details.
      • NoSQL DB (e.g., Cassandra, DynamoDB): For storing watch history, recommendations, and metadata about content.

  2. Key Components and Their Interactions:

    • User Management:
      • Handles registration, login, preferences, and subscription tracking.
      • Implements JWT for secure authentication.
    • Media Encoding:
      • Converts content into multiple resolutions and formats (adaptive bitrate streaming).
      • Ensures compatibility across devices and network conditions.
    • Content Delivery:
      • Leverages CDNs to deliver media files efficiently, using edge caching to minimize latency.
    • Recommendation Engine:
      • Implements machine learning models to suggest personalized content based on user behavior (e.g., watch history, ratings).
    • Search and Filtering:
      • Provides a robust search service with filters like genre, language, and release year.
      • Utilizes Elasticsearch for fast and efficient search capabilities.
    • Playback Service:
      • Manages video playback, quality adjustments, skipping, and buffering.
      • Adapts video quality dynamically based on network speed (adaptive streaming).
    • Billing System:
      • Handles subscription payments, renewals, and cancellations.
      • Integrates with third-party payment gateways for secure transactions.
    • Content Licensing Management:
      • Tracks licensing agreements for different regions.
      • Enforces geofencing to comply with copyright restrictions.

Low-Level Design for Selected Components

  1. Playback Service:

    • Adaptive Bitrate Streaming:
      • Divide videos into small chunks (e.g., 10 seconds).
      • Encode each chunk at multiple resolutions (e.g., 480p, 720p, 1080p).
      • Select appropriate resolution based on user bandwidth.
    • API Endpoints:
      • /startPlayback: Initiates playback by fetching the initial video chunk.
      • /fetchChunk: Serves subsequent chunks dynamically.
      • /updateQuality: Adjusts quality based on bandwidth changes.
    • Workflow:
      1. Client requests a video.
      2. Playback Service retrieves chunks from CDN.
      3. Video quality is adjusted dynamically during playback.

  2. Recommendation Engine:

    • Algorithm:
      • Collaborative Filtering: Suggests content based on what similar users have watched.
      • Content-Based Filtering: Suggests content similar to what the user has previously watched.
    • Data Pipeline:
      • Collects user interactions like views, ratings, and searches.
      • Processes data using Apache Kafka for real-time updates and Spark for batch processing.
    • Storage:
      • Stores pre-computed recommendations in a NoSQL database for fast retrieval.

  3. Content Delivery Network (CDN):

    • Edge Caching:
      • Stores frequently accessed videos close to the user’s location.
      • Reduces latency by avoiding long-distance data transfers.
    • Redundancy:
      • Mirrors content across multiple CDN servers to ensure availability.

  4. Billing System:

    • Database Schema:

      CREATE TABLE Subscriptions (
  user_id INT NOT NULL,
  plan_id INT NOT NULL,
  start_date DATE,
  end_date DATE,
  payment_status ENUM('SUCCESS', 'FAILED', 'PENDING')
);

    • API Endpoints:

      • /createSubscription: Starts a new subscription.
      • /processPayment: Processes payments through the payment gateway.
      • /cancelSubscription: Cancels an active subscription.

Challenges and Solutions

  1. Minimizing Buffering for Users with Varying Network Speeds:

    • Solution:
      • Implement adaptive bitrate streaming to adjust video quality dynamically.
      • Use CDNs with global coverage to minimize latency.
      • Preload video chunks to ensure smooth playback.

  2. Ensuring Copyright Compliance Across Regions:

    • Solution:
      • Enforce geofencing based on user location to restrict access to region-specific content.
      • Maintain a robust content licensing database to track agreements.

  3. Managing Huge Storage for Video/Audio Files:

    • Solution:
      • Use distributed storage solutions like Amazon S3 or Google Cloud Storage.
      • Implement tiered storage to archive older or less-viewed content.

  4. Optimizing Recommendation Algorithms:

    • Solution:
      • Continuously update models with real-time user data.
      • Use hybrid recommendation approaches (collaborative + content-based filtering).
      • Monitor algorithm performance and fine-tune for better engagement.

Final System Workflow Example

  1. User Flow:
    • User logs in (User Management authenticates using JWT).
    • User searches for a movie (Search Service retrieves data from Elasticsearch).
    • User selects and plays a video (Playback Service fetches chunks via CDN).
    • Recommendation Engine updates suggestions based on watch history.
    • Billing System handles subscription payments and renewals.