What is Netflix built on?

what is netflix built on: 260 million subscriber engineering

what is netflix built on raises questions about technical stability and global reach. Understanding the foundational components helps clarify how a platform maintains service for a massive audience. Learning about the system structure reveals the requirements for managing heavy digital traffic effectively.

The Architecture Behind the Play Button

Netflix is built on a massive, cloud-native netflix microservices architecture primarily hosted on Amazon Web Services (AWS). Its backend relies heavily on Java and Spring Boot, while Cassandra handles data storage and a custom Content Delivery Network (CDN) called Open Connect streams the actual video files.

Netflix handles around 15% of the entire worlds internet bandwidth during peak hours. ^[1] That scale requires serious engineering. Lets be honest: building a system that serves over 260 million subscribers without crashing on Friday nights is harder than it looks.

In my earlier days building streaming apps, we tried hosting video files on standard cloud storage. Disaster. The latency was brutal, and server costs skyrocketed within a week. Netflix solved this elegantly by splitting their infrastructure into two distinct ecosystems: the control plane and the data plane.

The Control Plane vs. The Data Plane

Understanding what is netflix built on requires separating the application logic from the video delivery. AWS handles everything before you hit play, while Open Connect takes over the second the video starts.

The control plane - running primarily on netflix backend architecture aws - manages user authentication, recommendations, search, and billing. It uses over 100,000 EC2 instances dynamically scaling based on traffic. ^[2] But heres the thing: AWS doesnt stream the video. That is the data plane.

how does netflix open connect work involves custom-built servers physically installed inside local Internet Service Provider (ISP) networks worldwide. Rarely have I seen an architectural decision save this much money and latency. By caching content locally, they reduce latency and costs significantly and bypass much of the congested public internet backbone. ^[3]

Backend and Microservices: The Java Engine

The backend architecture consists of thousands of independent microservices communicating with each other. Most of these core backend services are written in Java using the Spring Boot framework.

Why Java? It offers the robust tooling and performance needed for enterprise scale. But managing hundreds of microservices introduces massive complexity (and yes, it can be a debugging nightmare). To handle this, they originally built tools like Eureka for service discovery and Zuul for API routing.

Recently, they have been migrating toward federated GraphQL. This allows frontend teams to query exactly the data they need, reducing over-fetching and improving efficiency in some client applications. ^[4] I used to think REST was perfectly fine for any scale. Turns out, when you are sending data to thousands of different device types, over-fetching data is a luxury you cannot afford.

Data Storage: Handling Billions of Daily Events

Netflix does not use a single database type; it uses polyglot persistence to match the tool to the task. Apache Cassandra handles high-volume data like viewing history, while CockroachDB manages distributed transactional data.

Cassandra is a NoSQL database designed for high availability and no single point of failure. If an AWS region goes completely offline, Cassandra keeps serving data from another region seamlessly.

For caching, they use EVCache, which stores frequently accessed data in RAM. Your database stops crying. Instead of hammering the primary cluster with 10,000 identical queries per second, EVCache serves responses instantly. Meanwhile, Apache Kafka handles event streaming, processing trillions of daily events for analytics and recommendations.

Chaos Engineering: Breaking Things on Purpose

Netflix pioneered Chaos Engineering to ensure their system survives unexpected failures. Tools like Chaos Monkey intentionally terminate running servers in production to test system resilience.

It sounds insane. Why break your own production environment? I thought the exact same thing when I first heard about it. But in a distributed system, failures are inevitable. If you do not test for them, they will surprise you.

When you are dealing with a globally distributed user base that suddenly all decides to log in at exactly 8:00 PM on a Friday to watch a new season premiere, traditional scaling approaches simply collapse under the sheer volume of concurrent database connections. Chaos Monkey forces engineers to build stateless microservices with proper fallbacks. In reality, this preventative approach significantly improves system resilience and reduces the impact of outages. ^[5]

API Architecture Evolution at Netflix

REST API (Legacy)

• High - the client has to orchestrate multiple calls and filter out unnecessary data.

• Requires multiple round trips to different endpoints to assemble a complete view.

• Fixed endpoints that often return more data than the specific client needs (over-fetching).

Federated GraphQL (Current Recommended)

• Low - the complexity is shifted to the backend gateway, simplifying frontend code.

• A single request to the gateway fetches aggregated data from multiple microservices.

• Clients request exactly the fields they need using a flexible query language.

Scaling Video Delivery at StreamTech

James, a lead architect at a London-based media startup, faced a crisis when their app hit 50,000 daily active users. Their monolithic Node.js backend was taking 4 seconds to load the homepage. The team was exhausted from weekend outages.

First attempt: They tried vertical scaling by simply renting more expensive AWS servers. Costs tripled to $15,000 monthly, but the database bottlenecks remained. The single SQL database locked up constantly during peak evening hours.

The breakthrough came when James studied the Netflix architecture. He realized they needed to separate read-heavy operations from write-heavy tasks. They migrated the movie catalog to a distributed NoSQL database and added a Redis caching layer for the homepage.

Within three weeks, homepage load times dropped to 200ms, a 95% improvement. Server costs stabilized, and James finally slept through a Friday night without a pager alert. He learned that perfect performance is not the goal; resilient architecture is.