Netflix’s Microservices Architecture - rnakidi/dsa GitHub Wiki

Netflix’s Microservices Architecture explained:

Streaming to millions of users worldwide demands an architecture that is scalable, resilient, & highly available.

Netflix achieves it through a sophisticated microservices architecture on AWS, which enables modular and independently scalable components.

Here's a breakdown of its workflow and key insights:

  1. Client Devices — Netflix supports a wide array of client devices -TVs, smartphones, tablets, & gaming consoles. Each interaction (e.g., browsing or streaming) sends requests to Netflix's backend. This multi-device accessibility is seamless because of robust APIs.

  2. AWS Elastic Load Balancer (ELB) — As requests flood in, the Elastic Load Balancer acts as the gatekeeper. It distributes the workload across multiple backend services, ensuring no single server becomes a bottleneck. This mechanism is vital for fault tolerance and maintaining high throughput during peak usage.

  3. API Gateway Service — The API Gateway acts as a bridge between clients and microservices. It: • Routes requests to specific services (e.g., Login, Recommendations). • Manages rate-limiting to prevent abuse. • Handles request caching to optimize response times. This design centralizes request management while simplifying backend operations.

  4. Application APIs (Signup, Discovery, Play) — Netflix employs a microservice-first approach, where APIs like "Signup", "Discovery", and "Play" are autonomous services with their own logic and databases. Their independence ensures seamless updates, fault isolation, and scalability for specific features, without disrupting the entire ecosystem.

  5. Cache for Speed — Caching is critical for reducing latency. Frequently requested data—like trending titles or user preferences—is stored in high-performance caches, minimizing database queries and enhancing the user experience.

  6. Microservices Backbone — Netflix's microservices ecosystem represents hundreds of services, each dedicated to specific functionalities (e.g., video encoding, recommendations). Their independent deployment pipelines enable rapid innovation, as teams can deploy updates without impacting other services.

  7. Datastores Microservices utilize purpose-built databases (e.g., NoSQL for fast reads or relational DBs for structured data). This flexibility ensures optimal storage solutions tailored to each workload, bolstering both performance and scalability.

  8. Stream Processing Pipeline (AWS S3, Hadoop) — Real-time analytics power Netflix’s personalization engine. User interactions feed into a stream processing pipeline, leveraging tools like Hadoop for big data processing and AWS S3 for scalable storage. This pipeline drives data-driven decisions, improving recommendations, detecting anomalies, and optimizing content delivery.

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