Instagram Photo Sharing at Scale
How Instagram handles billions of photos: storage, processing, delivery, and scaling challenges.
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Architecture Evolution
Instagram's journey from a simple Django app to a global platform handling billions of users, with strategic pivots to support new features like Stories and video content.
1
Django Monolith
2010-2012100M usersSingle Django app on AWS
Key Challenge: Rapid user growth, single database
2
Database Sharding
2012-2014300M usersPostgreSQL sharding, Redis caching
Key Challenge: Photo storage scaling, feed generation
3
Stories & Video
2014-2016500M usersCassandra, custom media pipeline
Key Challenge: Real-time features, video processing
4
Global Platform
2016-Present2B+ usersMicroservices, ML-powered feeds
Key Challenge: Personalization at scale, global CDN
Photo Storage & Delivery Challenges
1
Massive Storage Requirements
Solution:
Multi-tier storage with automatic archiving
Scale:
100+ petabytes of photos
Impact:
40% cost reduction vs single-tier storage
2
Global Image Delivery
Solution:
CDN with 200+ edge locations
Scale:
500M+ photos served/day
Impact:
<200ms average load time globally
3
Multiple Image Formats
Solution:
Real-time image resizing and optimization
Scale:
20+ format variants per photo
Impact:
60% bandwidth savings
4
Duplicate Detection
Solution:
Perceptual hashing for near-duplicates
Scale:
Scan billions of images
Impact:
15% storage space saved
Feed Generation System
Feed Performance Optimization
Feed Load Time
3000ms2012
400ms2024
User Engagement
20min/dayChronological
45min/dayML-Ranked
Content Relevance
30%Time-based
85%Personalized
Ranking Factors
User Relationships
Close friends weighted 10x higher
Content Interaction
Likes, comments, shares, saves
Recency & Time
Decay function with user activity patterns
Feed Generation Pipeline
Candidate Selection
From user's network
~1000 posts
ML Ranking
Real-time scoring
< 50ms
Final Feed
Top ranked content
~50 posts
Cache Duration
Balance freshness vs load
5 minutes
Stories Feature Architecture
Upload Pipeline
• Video transcoding (multiple formats)
• Image optimization & compression
• CDN pre-warming for viral content
• Real-time upload progress
Delivery System
• Preloading next 3 stories
• Adaptive bitrate streaming
• Edge caching with 24hr TTL
• Mobile-first optimization
Auto-Deletion
• 24-hour lifecycle management
• Automated cleanup jobs
• Archive system for highlights
• Privacy compliance features
Stories Performance Metrics
Daily Stories Created
Peak during events and holidays
500M+
Average View Rate
Higher engagement than feed posts
70%
Load Time
Aggressive preloading strategy
< 1 second
Storage Cleanup
99.9% successful deletions
Automatic
Core Platform Services
Photo Upload ServicePython, Pillow, S3
Purpose:
Image processing and storage
Scale:
500M+ photos/day
Feed GenerationMachine Learning, Redis
Purpose:
Personalized timeline creation
Scale:
2B+ feed requests/day
Notification ServiceKafka, WebSockets
Purpose:
Real-time user notifications
Scale:
100B+ notifications/day
Stories ServiceRedis, CDN optimization
Purpose:
Ephemeral content delivery
Scale:
500M+ daily viewers
Key Architectural Lessons
Critical Decisions
- • Simple tech stack initially (Django) enabled rapid iteration
- • Database sharding by user ID for predictable growth
- • Heavy investment in CDN for global photo delivery
- • ML-powered feed ranking increased engagement significantly
- • Stories feature drove massive user growth and retention
Scale Challenges
- • Photo storage costs growing faster than revenue
- • Feed generation latency at billion-user scale
- • Real-time features (Stories) vs eventual consistency
- • Mobile-first optimization for global markets
- • Content moderation at massive scale