Social Media Platforms - HLD Architecture 📱

Core Concept

Key Insight: Social media platforms are content distribution systems at massive scale, optimized for real-time engagement, personalized feeds, and viral content propagation while handling billions of users and posts.

The Scale Problem

Daily Active Users:
├── Facebook: 2+ billion
├── Instagram: 1+ billion
├── Twitter: 400+ million
├── LinkedIn: 300+ million
└── TikTok: 1+ billion

Content Volume:
├── 95 million photos/day (Instagram)
├── 500 million tweets/day (Twitter)
├── 4+ billion posts/day (Facebook)
└── 1 billion videos/day (TikTok)

Core Technical Challenges

Challenge	Impact	Complexity
Feed Generation	Personalized content for billions	O(users × content) scaling
Real-time Updates	Instant notifications/reactions	WebSocket connections at scale
Content Storage	Petabytes of media files	CDN + blob storage optimization
Search & Discovery	Find relevant content/people	Distributed search indexing
Viral Content Handling	Traffic spikes during trending	Auto-scaling + load balancing

2. Instagram Architecture

Core Components

Instagram HLD:
├── User Service (profiles, authentication)
├── Media Service (photo/video upload & processing)
├── Feed Service (timeline generation)
├── Activity Service (likes, comments, shares)
├── Discovery Service (explore, hashtags)
├── Messaging Service (direct messages)
└── Notification Service (push notifications)

Feed Generation Strategy

Problem: Generate personalized feeds for 1B+ users in real-time

Solution - Hybrid Approach:

1. Pull Model (Timeline Service)
   ├── User requests feed
   ├── Query followed users' recent posts
   ├── Rank by engagement algorithm
   └── Return top N posts

2. Push Model (Fanout Service)
   ├── User posts new content
   ├── Push to all followers' timelines
   ├── Pre-compute timelines for active users
   └── Store in timeline cache

3. Hybrid Model (Best of Both)
   ├── Push for users with <1M followers
   ├── Pull for celebrities/influencers
   ├── Machine learning ranking
   └── Real-time personalization

Content Delivery Architecture

Media Upload Flow:
User App → CDN Edge → Media Processing → Multiple Resolutions → Global CDN

Processing Pipeline:
├── Image: Generate thumbnails (150x150, 320x320, 640x640, 1080x1080)
├── Video: Transcode to multiple bitrates (240p, 480p, 720p, 1080p)
├── Compression: Optimize file sizes without quality loss
└── Storage: Distribute across geographic regions

Key Design Decisions

Photo-First Architecture: Optimized for visual content
Chronological + Algorithmic Feed: Balance recency with engagement
Stories Feature: Ephemeral content reduces storage costs
Reels Integration: Short-form video competition with TikTok

3. LinkedIn Architecture

Professional Network Focus

LinkedIn HLD:
├── Profile Service (professional profiles, skills)
├── Connection Service (professional networking)
├── Feed Service (professional content timeline)
├── Job Service (job postings, applications)
├── Messaging Service (professional communication)
├── Learning Service (courses, certifications)
├── Sales Navigator (B2B lead generation)
└── Analytics Service (profile views, post metrics)

LinkedIn Feed Algorithm

Goal: Surface professionally relevant content

Ranking Factors:

Content Scoring:
├── Professional Relevance (40%)
│   ├── Industry alignment
│   ├── Job function similarity
│   ├── Skill overlap
│   └── Company connections
├── Engagement Signals (30%)
│   ├── Comments > Likes > Views
│   ├── Share rate and viral coefficient
│   ├── Time spent reading
│   └── Click-through rates
├── Recency & Freshness (20%)
│   ├── Post timestamp
│   ├── Trending topics in network
│   └── Real-time engagement velocity
└── Personal Connection (10%)
    ├── 1st/2nd/3rd degree connections
    ├── Direct message history
    └── Profile interaction frequency

Professional Graph Architecture

Relationship Mapping:
├── 1st Degree: Direct connections (mutual acceptance)
├── 2nd Degree: Friends of friends (network expansion)
├── 3rd Degree: Extended network reach
├── Company Connections: Current/former colleagues
├── Educational Connections: Alumni networks
└── Industry Connections: Professional similarity

Key Differentiators

B2B Focus: Professional content prioritization
Skill-Based Matching: Expertise and endorsements
Job Marketplace Integration: Recruitment platform
Long-Form Content: Articles and professional insights

4. Twitter/X Architecture

Real-Time Information Network

Twitter HLD:
├── Tweet Service (compose, publish, retrieve)
├── Timeline Service (home, mentions, lists)
├── Trend Service (hashtags, viral content)
├── Search Service (real-time tweet search)
├── Notification Service (mentions, likes, retweets)
├── Direct Message Service (private messaging)
├── Media Service (photos, videos, GIFs)
└── Advertising Service (promoted tweets/accounts)

Timeline Generation - Fan-out Architecture

Challenge: Deliver tweets to millions of followers instantly

Fan-out Strategies:

1. Fan-out on Write (Push)
   ├── User tweets → Push to all followers' timelines
   ├── Pros: Fast read times, pre-computed timelines
   ├── Cons: Expensive for users with millions of followers
   └── Used for: Regular users (<10K followers)

2. Fan-out on Read (Pull)
   ├── User requests timeline → Pull from followed accounts
   ├── Pros: Efficient for high-follower accounts
   ├── Cons: Slower read times, compute on demand
   └── Used for: Celebrities, verified accounts

3. Hybrid Approach
   ├── Most users: Fan-out on write
   ├── Celebrities: Fan-out on read
   ├── Mixed timelines: Merge cached + real-time content
   └── Smart caching based on user activity patterns

Real-Time Features

Live Updates Architecture:
├── WebSocket connections for active users
├── Server-Sent Events for timeline updates
├── Push notifications for mobile apps
├── Real-time trending algorithm updates
└── Live event integration (sports, news, politics)

Objective: Identify viral content and emerging topics in real-time

Factors:

Tweet volume velocity (mentions per minute)
Engagement rate acceleration
Geographic distribution of mentions
Influencer participation
Breaking news detection

5. Facebook Architecture

Multi-Service Platform

Facebook HLD:
├── User Service (profiles, friends, family)
├── News Feed Service (algorithmic timeline)
├── Post Service (status, photos, videos, stories)
├── Reaction Service (likes, reactions, comments)
├── Group Service (communities, discussions)
├── Page Service (business pages, fan engagement)
├── Event Service (social events, RSVP)
├── Marketplace Service (local commerce)
├── Messenger Service (chat, voice, video calls)
├── Gaming Service (social games, streaming)
└── Advertising Service (targeted ads, business tools)

News Feed Ranking Algorithm

Goal: Maximize user engagement and time spent on platform

EdgeRank Algorithm Evolution:

Modern Feed Ranking (2024):
├── Relationship Score (35%)
│   ├── Interaction frequency with poster
│   ├── Message history and mutual friends
│   ├── Profile visits and photo tags
│   └── Real-world relationship indicators
├── Content Type Performance (25%)
│   ├── Video content prioritization
│   ├── Live video boost during broadcast
│   ├── Image posts vs text-only content
│   └── Link click-through rates
├── Recency & Timeliness (20%)
│   ├── Post timestamp and decay function
│   ├── Trending topics and viral content
│   ├── Breaking news and real-time events
│   └── User's active hours optimization
└── Individual Preferences (20%)
    ├── Content category preferences
    ├── Historical engagement patterns
    ├── Hiding/unfollowing behavior
    └── Time spent per content type

Friend Network Architecture:
├── Adjacency Lists: User connections storage
├── Graph Databases: Neo4j for complex relationship queries
├── Caching Layer: Redis for frequent friend lookups
├── Sharding Strategy: Geographic and social cluster-based
└── Privacy Controls: Granular visibility and sharing settings

6. Common Architectural Patterns

Feed Generation Patterns

Pattern	Use Case	Pros	Cons
Push (Fan-out on Write)	Regular users	Fast reads	Expensive writes for influencers
Pull (Fan-out on Read)	Celebrities	Efficient writes	Slower reads
Hybrid	Mixed user base	Balanced performance	Complex implementation

Content Storage Architecture

Media Storage Strategy:
├── Hot Storage (Recent, popular content)
│   ├── SSD-based storage for fast access
│   ├── Multiple CDN regions
│   └── High replication factor
├── Warm Storage (Older, moderate access)
│   ├── HDD-based storage
│   ├── Regional CDN caching
│   └── Reduced replication
└── Cold Storage (Archive, rare access)
    ├── Glacier/tape storage
    ├── Single region backup
    └── Minimal replication

Notification System Design

Push Notification Architecture:
├── Event Triggers (likes, comments, mentions, messages)
├── User Preference Engine (notification settings)
├── Delivery Channels (iOS/Android push, email, SMS, web)
├── Rate Limiting (prevent notification spam)
├── Personalization (send time optimization)
└── Analytics (delivery rates, engagement metrics)

7. Search & Discovery Systems

Search Architecture

Social Media Search Components:
├── Real-time Indexing (new posts/profiles)
├── Full-text Search (Elasticsearch/Solr)
├── People Search (fuzzy matching, social graph)
├── Hashtag/Trend Search (real-time aggregation)
├── Semantic Search (ML-based content understanding)
└── Personalized Results (user context and history)

Recommendation Engines

Content Discovery Strategies:

Collaborative Filtering: "Users like you also liked..."
Content-Based Filtering: Similar content to user's interests
Social Signals: Friends' activities and recommendations
Trending Content: Viral and popular posts
Geographic Relevance: Location-based content
Temporal Patterns: Time-sensitive content optimization

8. Scalability & Performance Patterns

Database Architecture

Social Media Data Patterns:
├── User Data (RDBMS)
│   ├── MySQL/PostgreSQL for ACID compliance
│   ├── User profiles, settings, relationships
│   └── Master-slave replication
├── Content Data (NoSQL)
│   ├── MongoDB/Cassandra for horizontal scaling
│   ├── Posts, comments, reactions
│   └── Eventually consistent
├── Timeline Data (Cache)
│   ├── Redis/Memcached for speed
│   ├── Pre-computed user timelines
│   └── TTL-based expiration
└── Media Files (Object Storage)
    ├── S3/GCS for blob storage
    ├── CDN distribution
    └── Geographic replication

Caching Strategies

Multi-Layer Caching:

Browser Cache: Static assets (CSS, JS, images)
CDN Cache: Media files and popular content
Application Cache: User sessions, frequent queries
Database Cache: Query result caching
Timeline Cache: Pre-computed user feeds

Load Balancing

Traffic Distribution:
├── DNS Load Balancing (geographic routing)
├── Layer 7 Load Balancing (application-aware)
├── API Gateway (rate limiting, authentication)
├── Microservice Mesh (service-to-service)
└── Database Load Balancing (read/write separation)

9. Real-Time Features

Live Updates Architecture

Real-Time Communication:
├── WebSocket Servers (persistent connections)
├── Server-Sent Events (one-way updates)
├── Message Queues (Kafka, RabbitMQ)
├── Pub/Sub Systems (Redis, Apache Pulsar)
└── Push Notification Services (FCM, APNS)

Event-Driven Architecture

Key Events:

User actions (post, like, comment, share)
System events (trending detection, spam filtering)
External events (breaking news, sports scores)
Scheduled events (content cleanup, analytics)

10. Content Moderation & Safety

Automated Content Moderation

Multi-Layer Moderation:
├── Upload Filters
│   ├── Image recognition (inappropriate content)
│   ├── Text analysis (hate speech, spam)
│   ├── Video content scanning
│   └── Audio content analysis
├── Post-Upload Monitoring
│   ├── User reporting systems
│   ├── Automated flagging algorithms
│   ├── Community-based moderation
│   └── Expert human review
├── Behavioral Analysis
│   ├── Bot detection algorithms
│   ├── Fake account identification
│   ├── Coordinated inauthentic behavior
│   └── Spam pattern recognition
└── Global Policy Enforcement
    ├── Regional content compliance
    ├── Age-appropriate content filtering
    ├── Misinformation detection
    └── Violence and extremism prevention

11. Analytics & Machine Learning

User Behavior Analytics

Data Collection:
├── User Interactions (clicks, scrolls, time spent)
├── Content Performance (engagement rates, reach)
├── Social Graph Analysis (connection patterns)
├── Device and Platform Usage
└── Geographic and Temporal Patterns

ML Applications:
├── Feed Ranking Algorithms
├── Content Recommendation Systems
├── Ad Targeting and Optimization
├── Spam and Abuse Detection
├── Trend Prediction and Analysis
└── User Lifetime Value Prediction

12. Platform-Specific Innovations

Instagram

Stories Architecture: Ephemeral content with 24-hour TTL
Reels System: Short-form video with music integration
Shopping Integration: E-commerce within social posts
AR Filters: Real-time face tracking and augmentation

Professional Graph: Skill-based connections and endorsements
Content Quality Filters: Professional relevance scoring
Job Matching Engine: AI-powered recruitment platform
Learning Platform Integration: Course completion tracking

Twitter/X

Real-Time Trending: Sub-minute trend detection
Character Limit Optimization: Concise content prioritization
Thread Architecture: Connected tweet sequences
Spaces Integration: Live audio conversation platform

Facebook

Multi-App Integration: WhatsApp, Instagram, Messenger sync
VR/AR Integration: Metaverse platform development
Marketplace Platform: Local commerce integration
Gaming Ecosystem: Social gaming and streaming

Key Architecture Principles

✅ Event-Driven Design: Real-time updates and notifications ✅ Microservices Architecture: Independent service scaling ✅ Content-First Storage: Optimize for media-heavy workloads ✅ Feed Personalization: ML-driven content ranking ✅ Global CDN Strategy: Low-latency content delivery ✅ Horizontal Scalability: Handle traffic spikes and growth ✅ Real-Time Processing: Live updates and trending detection ✅ Privacy by Design: User data protection and control

Bottom Line: Social media platforms are complex distributed systems that must balance personalization, real-time interaction, content discovery, and safety at unprecedented scale while maintaining sub-second response times for billions of daily active users.

Core Concept​

1. Common Social Media Challenges​

The Scale Problem​

Core Technical Challenges​

2. Instagram Architecture​

Core Components​

Feed Generation Strategy​

Content Delivery Architecture​

Key Design Decisions​

3. LinkedIn Architecture​

Professional Network Focus​

LinkedIn Feed Algorithm​

Professional Graph Architecture​

Key Differentiators​

4. Twitter/X Architecture​

Real-Time Information Network​

Timeline Generation - Fan-out Architecture​

Real-Time Features​

Trending Algorithm​

5. Facebook Architecture​

Multi-Service Platform​

News Feed Ranking Algorithm​

Social Graph Storage​

6. Common Architectural Patterns​

Feed Generation Patterns​

Content Storage Architecture​

Notification System Design​

7. Search & Discovery Systems​

Search Architecture​

Recommendation Engines​

8. Scalability & Performance Patterns​

Database Architecture​

Caching Strategies​

Load Balancing​

9. Real-Time Features​

Live Updates Architecture​

Event-Driven Architecture​

10. Content Moderation & Safety​

Automated Content Moderation​

11. Analytics & Machine Learning​

User Behavior Analytics​

12. Platform-Specific Innovations​

Instagram​

LinkedIn​

Twitter/X​

Facebook​

Key Architecture Principles​