Netflix Architecture Case Study

An in-depth look at Netflix’s highly scalable distributed system architecture that serves 200+ million subscribers worldwide.

Architecture Overview

Netflix operates one of the world’s largest and most sophisticated microservices architectures, processing billions of API requests daily.

                    ┌─────────────────────────────────────────────┐
                    │              CDN (Open Connect)              │
                    │         Content Delivery Appliances          │
                    └─────────────────┬───────────────────────────┘
                                      │
                    ┌─────────────────▼───────────────────────────┐
                    │            API Gateway (Zuul)                │
                    │        Load Balancing, Routing               │
                    └─────────────────┬───────────────────────────┘
                                      │
    ┌─────────────────────────────────┼─────────────────────────────────┐
    │                                 │                                 │
    ▼                                 ▼                                 ▼
┌───────────┐                   ┌───────────┐                   ┌───────────┐
│ Playback  │                   │ Discovery │                   │  Account  │
│ Service   │                   │  Service  │                   │  Service  │
└───────────┘                   └───────────┘                   └───────────┘

Key Components

1. Open Connect CDN

• Purpose: Deliver video content efficiently
• Implementation: Custom CDN with appliances in ISP networks
• Scale: Handles 15% of global internet traffic
• Features:
  • Content pre-positioned close to users
  • Intelligent content routing
  • Real-time traffic optimization

2. API Gateway (Zuul)

• Function: Entry point for all client requests
• Responsibilities:
  • Request routing
  • Load balancing
  • Authentication
  • Rate limiting
  • Dynamic filtering
• Open Source: Netflix OSS contribution

3. Service Discovery (Eureka)

@EnableEurekaClient
@SpringBootApplication
public class MyServiceApplication {
    // Service registers with Eureka
}

• Services register themselves
• Clients discover services dynamically
• Health monitoring
• Automatic failover

4. Circuit Breaker (Hystrix)

// Conceptual C# equivalent using Polly
var policy = Policy
    .Handle<HttpRequestException>()
    .CircuitBreakerAsync(
        exceptionsAllowedBeforeBreaking: 5,
        durationOfBreak: TimeSpan.FromSeconds(30)
    );

• Prevents cascade failures
• Fast failure response
• Fallback mechanisms
• Real-time monitoring

5. Client-Side Load Balancing (Ribbon)

• Distributes load across service instances
• Multiple algorithms (round-robin, weighted, zone-aware)
• Integrated with service discovery

Data Architecture

Primary Data Stores

Caching Strategy

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Client    │────▶│   EVCache   │────▶│  Cassandra  │
│   Request   │     │   (Cache)   │     │  (Source)   │
└─────────────┘     └─────────────┘     └─────────────┘

• EVCache: Distributed caching layer
• Multi-tier caching
• Cache warming strategies

Resilience Patterns

Chaos Engineering (Chaos Monkey)

Netflix pioneered chaos engineering to test system resilience:

1. Chaos Monkey: Randomly terminates instances
2. Latency Monkey: Introduces artificial delays
3. Conformity Monkey: Finds non-conforming instances
4. Janitor Monkey: Cleans up unused resources
5. Chaos Kong: Simulates entire region failures

Bulkhead Pattern

Isolate components to prevent cascade failures:

┌──────────────────────────────────────┐
│           Application                 │
├──────────┬──────────┬───────────────┤
│ Pool A   │  Pool B  │    Pool C     │
│ (Auth)   │ (Search) │  (Recommend)  │
└──────────┴──────────┴───────────────┘

Recommendation Engine

Architecture

• Input: Viewing history, ratings, browsing behavior
• Processing: ML models on Spark clusters
• Output: Personalized content rankings

Data Pipeline

User Actions → Kafka → Spark Streaming → ML Models → Recommendations
                 │
                 └──→ Batch Processing → Model Training

Deployment & Operations

Continuous Deployment

• Spinnaker: Multi-cloud deployment platform
• Red/Black deployments
• Canary releases
• Automated rollbacks

Monitoring Stack

• Atlas: Time-series metrics
• Mantis: Real-time stream processing
• Vector: On-host performance monitoring

Key Lessons

1. Design for Failure

• Assume everything will fail
• Build redundancy at every level
• Test failure scenarios regularly

2. Embrace Microservices

• Small, focused services
• Independent deployment
• Clear API contracts

3. Automate Everything

• Deployment
• Scaling
• Recovery

4. Use Caching Aggressively

• Multiple cache layers
• Intelligent cache invalidation
• Edge caching for content

5. Invest in Observability

• Comprehensive metrics
• Distributed tracing
• Real-time alerting

Technologies Used

Sources

• Arhitectura/Netflix architecture.gif