Netflix Streaming Architecture
Explore Netflix's streaming architecture, CDN strategy, and how they serve billions of hours of content globally.
30 min read•Advanced
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Architecture Evolution
Netflix's transformation from monolith to microservices happened gradually over 9 years, driven by scale and the need for rapid innovation.
1
Monolithic DVD
2007-201010M subscribersSingle Java application
Key Challenge: Limited scalability, slow deployments
2
Cloud Migration
2010-201340M subscribersLift and shift to AWS
Key Challenge: Learning distributed systems
3
Microservices
2013-201680M subscribers500+ microservices
Key Challenge: Service coordination, monitoring
4
Global Platform
2016-Present230M+ subscribers1000+ microservices
Key Challenge: Global consistency, personalization
Core Platform Services
Netflix built and open-sourced many foundational microservices components that became industry standards.
Zuul (API Gateway)Java, filters
Purpose:
Route requests, authentication
Scale:
2M+ requests/sec
Eureka (Service Discovery)REST, heartbeats
Purpose:
Service registration/discovery
Scale:
10K+ services
Hystrix (Circuit Breaker)Bulkhead pattern
Purpose:
Fault tolerance, isolation
Scale:
Latency < 99th percentile
Ribbon (Load Balancer)Java, algorithms
Purpose:
Client-side load balancing
Scale:
Dynamic server lists
Content Delivery at Scale
Global CDN Performance
Cache Hit Rate
5%Origin
95%Edge
Response Time
200msOrigin
20msEdge
Bandwidth Cost
100%Origin
10%Edge
Content Strategy
Popular Content
Predictive caching for trending shows
100% Pre-cached
Long Tail Content
Generated on first request
On-demand
Live Events
Sports, award shows
Real-time
Encoding Pipeline
Multiple Bitrates
240p to 4K, adaptive streaming
Global Distribution
15,000+ servers in 200+ countries
Personalization at Scale
Data Collection
• 500+ billion events/day
• Viewing patterns, searches
• Device types, time of day
• Pause, rewind, fast-forward
ML Pipeline
• Collaborative filtering
• Deep learning models
• A/B testing framework
• Real-time inference
Results
• 80% of viewing from recs
• 2M+ A/B tests/year
• Sub-second response
• Personalized artwork
Recommendation Performance
Homepage Response
Personalized for 230M+ users
< 100ms
Model Training
Continuous learning from new data
Daily
A/B Test Impact
Typical engagement improvement
2-10%
Key Architectural Lessons
What Worked
- • Gradual migration over years, not big-bang
- • Investment in observability and tooling first
- • Culture of experimentation and failure tolerance
- • Service ownership by small teams
- • Open-sourcing for community feedback
Challenges
- • Distributed system complexity
- • Service dependency management
- • Debugging across 1000+ services
- • Data consistency across services
- • Operational overhead and tooling