Netflix Architecture

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Overview

Netflix has evolved from a simple DVD rental service to the world’s leading streaming platform, serving over 200 million subscribers across 190+ countries. This case study explores the architectural decisions, technology choices, and evolution that enable Netflix to stream billions of hours of content globally.

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Architecture Evolution

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The Four Stages of Netflix API Architecture

Stage 1: Monolith (Early Days)

The application was packaged and deployed as a single monolith - a Java WAR file. This approach is typical for most startups as it allows for rapid development and simple deployment.Challenges:

• Difficult to scale individual components
• Deployment risks affected entire application
• Limited team autonomy

Stage 2: Direct Access to Microservices

Client applications could make requests directly to individual microservices. As Netflix grew, they decomposed the monolith into hundreds of specialized services.Challenges:

• Exposing hundreds/thousands of microservices to clients was unmanageable
• Network chattiness and latency issues
• Difficult to maintain backward compatibility

Stage 3: Gateway Aggregation Layer

Many use cases span multiple services. For example, rendering the Netflix homepage requires data from movie, production, and talent APIs. The gateway aggregation layer solved this by:

• Consolidating multiple service calls
• Reducing client complexity
• Providing a unified API interface

Key Technologies:

• ZUUL - API Gateway
• Eureka - Service Discovery

Stage 4: Federated Gateway (Current)

As the number of developers grew and domain complexity increased, even the aggregation layer became difficult to maintain. Netflix adopted GraphQL Federation which allows:

• A single GraphQL gateway that fetches data from all other APIs
• Teams to own their domain’s schema independently
• Unified data graph across the organization

This architecture provides the best balance of centralization and team autonomy.

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Technology Stack

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Frontend

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Backend Services

Netflix’s backend is built on a microservices architecture with thousands of services:

• API Gateway: ZUUL (custom-built)
• Service Discovery: Eureka
• Framework: Spring Boot
• Communication: REST, gRPC, GraphQL

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Database Architecture

Netflix uses a polyglot persistence approach, selecting the right database for each use case:

Relational Databases

MySQL - Used for:

• Billing transactions
• Subscriptions
• Tax information
• Revenue tracking

CockroachDB - Used for:

• Multi-region active-active architecture
• Global transactions
• Data pipeline workflows

Columnar Databases

Used primarily for analytics:

• Redshift: Structured data warehouse
• Druid: Real-time analytics
• Spark: Data pipeline processing
• Tableau: Data visualization

Key-Value Store

EVCache (built on Memcached):

• Used for 10+ years across most services
• Caches Netflix homepage
• Stores personal recommendations
• Critical for low-latency access

Wide-Column Store

Cassandra - The default choice at Netflix:

• Video and actor information
• User data
• Device information
• Viewing history
• Highly scalable and available

Time-Series Database

Atlas (open-source, in-memory):

• Built by Netflix
• Metrics storage and aggregation
• Real-time operational insights

Object Storage

Amazon S3 - Stores almost everything:

• Image files
• Video content
• Metrics
• Log files

Apache Iceberg with S3:

• Big data storage
• Data lake architecture

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Caching Strategy

Netflix uses EVCache (a distributed key-value store) in four distinct patterns:

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1. Lookaside Cache

The most common caching pattern:

1. Application requests data from EVCache client
2. If cache miss, fetch from backend service and Cassandra
3. Store result in cache for future requests
4. Return data to application

Use Cases: General data caching, user profiles, content metadata

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2. Transient Data Store

Used for temporary session data:

• Playback session information
• One service starts the session
• Another updates it during playback
• Final service closes the session

Benefits: Fast access without database writes for temporary data

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3. Primary Store

EVCache as the source of truth:

• Netflix runs large-scale pre-compute systems nightly
• Computes personalized homepage for every user profile
• Based on watch history and recommendations
• Written directly to EVCache
• Online services read from cache to build homepage

Why: Faster than database queries for pre-computed results

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4. High Volume Data

For data requiring:

• High access volume
• High availability
• Low latency

Example: UI strings and translations on Netflix homepage

• Asynchronous process computes and publishes to EVCache
• Applications read with minimal latency

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Video Delivery

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Storage and Distribution

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How Video Streaming Works

1. Content Encoding: Videos encoded in multiple bitrates and resolutions
2. Distribution: Content pushed to Open Connect edge servers worldwide
3. Client Request: User selects content to watch
4. Server Selection: Nearest/optimal edge server selected
5. Adaptive Streaming: Bitrate adjusts based on network conditions

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Messaging and Streaming

Netflix employs event-driven architecture for real-time data processing:

• Apache Kafka: Message broker for event streaming
• Apache Flink: Stream processing for real-time analytics
• Use Cases:
  • User activity tracking
  • Real-time recommendations
  • Monitoring and alerting
  • A/B testing data

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CI/CD Pipeline

Netflix has one of the most mature CI/CD practices in the industry:

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Development Tools

• JIRA: Project planning and tracking
• Confluence: Documentation and collaboration
• Git: Version control

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Build and Test

• Jenkins: Continuous integration
• Gradle: Build automation
• Spinnaker: Multi-cloud continuous delivery (open-sourced by Netflix)

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Deployment

• Spinnaker: Orchestrates deployments across regions
• Canary Deployments: Test changes with small percentage of traffic
• Red/Black Deployments: Quick rollback capability

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Monitoring and Reliability

• Atlas: Time-series metrics
• PagerDuty: On-call management
• Chaos Monkey: Randomly terminates instances to test resilience
• Simian Army: Suite of chaos engineering tools

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Key Architecture Decisions

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1. Cloud-Native from the Start

• Netflix migrated to AWS in 2008-2016
• One of the largest AWS deployments
• Multi-region for disaster recovery
• Auto-scaling based on demand

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2. Polyglot Persistence

• Different databases for different use cases
• No single database solution
• Optimized for specific access patterns

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3. Service-Oriented Architecture

• Microservices for business capabilities
• Teams own services end-to-end
• Independent deployment and scaling

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4. API Gateway Evolution

• From monolith → direct access → gateway → federated gateway
• GraphQL for flexible data fetching
• Reduced network overhead

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5. Heavy Investment in Observability

• Custom tooling (Atlas, etc.)
• Distributed tracing
• Real-time anomaly detection

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Lessons Learned

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Scale Statistics

• 200+ million subscribers worldwide
• 190+ countries served
• Billions of hours streamed monthly
• Thousands of microservices
• Hundreds of thousands of streaming sessions concurrently
• Petabytes of video content

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References

• Netflix Tech Blog
• Netflix Open Source
• Netflix API Evolution
• Netflix Database Architecture