System Design for Social Media Platform

High-Level System Design

  1. Architecture Overview:

    • Microservices Architecture: Modular services for profiles, messaging, notifications, etc., ensure scalability and easier maintenance.
    • Database:
      • Relational DB (e.g., PostgreSQL): For structured data like user profiles and relationships.
      • NoSQL DB (e.g., MongoDB, Cassandra): For unstructured data like posts, comments, and media.
      • Graph Database (e.g., Neo4j): For relationships like friends, followers, and network traversal.
    • Load Balancer: Distributes requests across multiple servers for high availability.
    • Caching: Uses Redis or Memcached for frequently accessed data like feeds, profiles, and trending topics.
    • CDN: Stores and delivers media assets like images and videos efficiently.

  2. Key Components:

    • User Profiles:
      • Handles user information, settings, friends/followers list, and activity logs.
    • Feed System:
      • Displays posts from friends and followed accounts.
      • Implements feed-ranking algorithms based on relevance, time, and user preferences.
    • Real-time Messaging:
      • Enables chat with WebSocket for real-time updates.
      • Stores conversation history for asynchronous access.
    • Media Upload and Storage:
      • Processes and stores images/videos in multiple resolutions.
      • Uses object storage (e.g., AWS S3) for scalable storage.
    • Notifications System:
      • Alerts users about activities like likes, comments, mentions, and follows.
    • Recommendation System:
      • Suggests new connections, hashtags, or groups using machine learning models.
    • Analytics Service:
      • Tracks metrics like user activity, engagement rates, and ad performance.
    • Content Moderation:
      • Uses AI/ML to detect inappropriate content (e.g., hate speech, NSFW content).
      • Includes manual review workflows for flagged content.
    • Advertising System:
      • Allows businesses to run targeted ad campaigns using user demographics and interests.

Low-Level Design for Selected Components

  1. Feed System:

    • Architecture:
      • Pull-based model: Users fetch their feed when opening the app.
      • Feed-ranking service ranks posts based on user preferences, engagement history, and freshness.

    • Database Schema:

      CREATE TABLE Posts (
  post_id UUID PRIMARY KEY,
  user_id UUID NOT NULL,
  content TEXT,
  media_url TEXT,
  timestamp TIMESTAMP
);
        
CREATE TABLE Feed (
  user_id UUID,
  post_id UUID,
  ranking_score FLOAT,
  PRIMARY KEY (user_id, post_id)
);
    • Workflow:
      1. User opens the feed.
      2. Service queries the Feed table for the top-ranked posts.
      3. Posts are fetched and rendered on the UI.

  2. Real-Time Messaging:

    • Architecture:
      • WebSocket-based service for real-time communication.
      • Stores messages in a NoSQL database for quick read/write.
    • API Endpoints:
      • /sendMessage: Sends a message to another user.
      • /fetchMessages: Retrieves message history for a chat.
    • Workflow:
      1. User initiates a chat, and the client establishes a WebSocket connection.
      2. Messages are sent via WebSocket and stored in the database.
      3. Notifications are sent to the recipient in real time.

  3. Notifications System:

    • Architecture:
      • Event-driven architecture using Apache Kafka.
      • Microservices produce and consume notification events.
    • Workflow:
      1. When a user likes a post, an event is published to Kafka.
      2. Notification service consumes the event and sends a push notification to the user.
      3. Notifications are stored for asynchronous viewing.

  4. Content Moderation:

    • Workflow:
      • AI model scans uploaded content for inappropriate material (e.g., hate speech, NSFW).
      • Flagged content is sent for manual review if confidence is below a threshold.
    • Technology:
      • TensorFlow or PyTorch for AI models.
      • Pre-trained models for text and image classification.

  5. Advertising System:

    • Database Schema:

      CREATE TABLE Ads (
  ad_id UUID PRIMARY KEY,
  advertiser_id UUID NOT NULL,
  content TEXT,
  target_audience JSONB,
  budget FLOAT,
  impressions INT,
  clicks INT
);

    • Workflow:

      1. Advertiser uploads an ad and specifies the target audience.
      2. Ads are matched with users based on demographics, interests, and behavior.
      3. Impressions and clicks are tracked for billing and analytics.

Challenges and Solutions

  1. Handling Real-Time Updates and Massive User Interactions:

    • Solution:
      • Use WebSocket for real-time features like messaging and notifications.
      • Employ caching for frequently accessed data.
      • Scale horizontally with microservices and distributed databases.

  2. Ensuring Privacy and Data Security:

    • Solution:
      • Encrypt sensitive data (e.g., passwords, messages).
      • Implement strict access controls and GDPR-compliant workflows.
      • Use tokenization for secure user sessions.

  3. Combatting Misinformation and Spam Bots:

    • Solution:
      • Use AI to detect fake accounts and spam.
      • Flag and review posts spreading misinformation.
      • Implement CAPTCHA to reduce bot activity.

  4. Optimizing Feed Ranking Algorithms:

    • Solution:
      • Use machine learning models to rank posts based on engagement.
      • Continuously monitor and update algorithms for better performance.

Final System Workflow Example

  1. User Flow:
    • User logs in (User Management authenticates via JWT).
    • User uploads a photo (Media Service processes and stores it in CDN).
    • User opens their feed (Feed System retrieves ranked posts).
    • User sends a message (Messaging Service handles real-time delivery).
    • User receives a like notification (Notification System pushes an alert).