Netflix stands as a prime example of backend engineering excellence at scale. The platform’s seamless streaming, personalized recommendations, and consistent experience across devices are all underpinned by a sophisticated architecture built primarily on Java. While the tech landscape has seen the rise of languages like Kotlin, Go, and Rust, Netflix continues to rely on Java—not out of inertia, but because the language and its ecosystem have evolved to meet the demands of modern, high-throughput systems.

The Architectural Backbone: Federated GraphQL Platform

Netflix’s Federated GraphQL Architecture

At the core of Netflix’s backend is a federated GraphQL platform. This architecture acts as the main interface between client applications and backend data, allowing clients to request exactly what they need while enabling backend teams to evolve their services independently.

Every client query—whether from a smart TV, mobile device, or browser—reaches a centralized API Gateway. This gateway parses the request, breaks it into subqueries, and routes them to the appropriate backend services. Each backend team manages a Domain Graph Service (DGS), implemented as a Spring Boot application, which owns a portion of the overall GraphQL schema.

Key Features of the DGS Framework

• Built as an extension of Spring Boot
• Dependency injection, configuration, and lifecycle managed by Spring Boot
• GraphQL resolvers as annotated Spring components
• Integrated observability, security, retry logic, and service mesh features

This federated approach enables independent service deployment, schema-driven collaboration, and clear domain boundaries, such as separating recommendations from user profiles.

Microservice Fan-Out: Query Execution Flow

When a user requests information—like titles and images for several shows—the process involves multiple services:

• The API Gateway receives the request
• It contacts several DGSs to resolve fields like metadata, artwork, and availability
• Each DGS may further fetch data from stores or other services

This fan-out pattern adds flexibility but introduces complexity. Netflix employs aggressive timeouts, retry logic, and fallback strategies to prevent slow services from causing user-facing latency.

Protocol Choices: GraphQL and gRPC

Netflix uses HTTP and GraphQL for client-to-gateway communication, ensuring compatibility across devices. Internally, backend services communicate via gRPC, a high-performance binary protocol with strong typing and efficient service-to-service calls. This separation allows GraphQL to handle flexible client data needs, while gRPC excels at internal RPC interactions.

JVM Evolution: Upgrading the Java Stack

Breaking Free from Technical Debt

Netflix’s Java codebase was long anchored to JDK 8 due to dependencies on a custom in-house framework. Upgrading required patching incompatible libraries and migrating thousands of services to Spring Boot. Automated tooling helped standardize code transformation and deployment, resulting in a unified, modern platform.

Benefits of Upgrading to JDK 17+

• G1 garbage collector improvements: 20% less CPU time spent on GC, fewer and shorter pauses
• Higher throughput and better CPU utilization
• More reliable distributed systems due to reduced timeouts

Generational ZGC: Next-Level Garbage Collection

While the G1 garbage collector balanced throughput and pause times, it struggled under high concurrency, causing latency spikes and operational challenges. The introduction of generational ZGC in JDK 21 brought near-zero pause times and allowed clusters to run closer to CPU saturation without instability. A simple configuration change delivered smoother scaling and reduced error rates.

Leveraging Java Virtual Threads

Traditional concurrency models led to high thread counts and memory usage. With Java 21+, Netflix adopted virtual threads—lightweight, JVM-managed threads that enable scalable blocking code. This allowed parallel execution of GraphQL resolvers, reducing latency without requiring developers to change their coding style.

Addressing Trade-Offs

Early adoption revealed deadlocks due to thread pinning in synchronized blocks. This was resolved in JDK 24, allowing Netflix to fully embrace virtual threads and benefit from their performance and simplicity.

Moving Beyond RxJava and Reactive Complexity

Netflix pioneered reactive programming with RxJava, but the approach introduced complexity when mixed with traditional models. Virtual threads and structured concurrency now allow developers to express asynchronous workflows using straightforward code, reducing the need for reactive abstractions except in specialized scenarios like long IO chains or streaming workloads.

The Spring Boot Netflix Stack

Netflix standardizes backend services on a customized Spring Boot stack, integrating company-specific modules for security, observability, service mesh, gRPC, dynamic configuration, and retry logic. The platform remains closely aligned with upstream Spring Boot, with in-house tooling smoothing major upgrades—such as namespace migrations from javax.* to jakarta.*—to ensure compatibility and developer productivity.

Conclusion

Netflix’s Java architecture in 2025 exemplifies deliberate, ongoing optimization rather than reliance on legacy technology. By aggressively upgrading its stack, extending Spring Boot, adopting GraphQL, leveraging virtual threads, and tuning JVM infrastructure, Netflix continues to deliver scalable, resilient streaming services worldwide.

Key Takeaways:

• Java remains highly competitive when treated as a living ecosystem
• In-house platform ownership accelerates upgrades and innovation
• Virtual threads simplify concurrency and improve scalability
• Infrastructure tuning—beyond just code—drives reliability and performance

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