Best of Netflix — 2024

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Article
ByteByteGo·2y
How Netflix Manages 238 Million Memberships?
Netflix manages 238 million memberships through a microservices architecture that ensures high availability and scalability. The platform includes stages like signup, plan changes, renewals, and payment issues, with core components using databases like CockroachDB and Cassandra. The use of Change Data Capture (CDC) patterns helps in tracking historical data for debugging and analytics, ensuring robustness and resilience. The architecture supports large volumes of data and requests, crucial for handling the global scale and diverse offerings of Netflix.
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Article
ByteByteGo·2y
EP107: Top 9 Architectural Patterns for Data and Communication Flow
Learn about the top 9 architectural patterns for data and communication flow, how Netflix uses Java, and the most important AWS services to learn.
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3
Article
The New Stack·2y
Netflix Open Sources Maestro, a Next-Gen Data Workflow Engine
Netflix has released Maestro, an open-source workflow orchestrator under an Apache 2.0 license. Designed for scalability and extensibility, Maestro supports hundreds of thousands of workflows and millions of jobs daily. It integrates with various open-source technologies and handles tasks such as retries, queuing, and task distribution. Originally announced in 2022, Maestro was developed to address performance limitations of Netflix's previous system, Meson, and is optimized for large-scale data workflows in various formats.
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Article
System Design Codex·1y
How Netflix Uses Caching To Hold Attention?
Netflix employs EVCache, a distributed in-memory key-value store, to enhance streaming performance. Key use cases include look-aside cache for quicker data access, transient data store for real-time updates, primary store for precomputed user homepage data, and caching high-volume UI elements for seamless global access. These strategies minimize latency, improve scalability, and ensure a superior user experience by swiftly delivering personalized content.
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Article
Community Picks·2y
10 Things You Can Learn from Netflix’s Architecture
Netflix's architecture, supporting 247 million subscribers, employs a client-backend-CDN system structure to ensure efficient streaming and exceptional user experience. Leveraging AWS for backend scalability, Netflix uses 700 microservices for modularity and ease of maintenance, and implements geographical redundancy for reliability. Through its custom CDN, Open Connect Appliance (OCA), and intelligent content delivery mechanisms, Netflix enhances streaming quality and performance. Additional strategies like efficient transcoding, predictive caching, DRM, and adaptive network handling further ensure a seamless experience across diverse devices and network conditions.
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Article
InfoQ·2y
How Netflix Really Uses Java
Netflix discusses their use of Java, including their journey from Java 8 to Java 17, the benefits of upgrading, and their adoption of Spring Boot. They also explore the use of virtual threads, the DGS framework for GraphQL, and the evolution of their architecture. Overall, it highlights the importance of staying up-to-date with technology and the value of performance improvements.
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Article
Cerbos·1y
Best practices for testing and deploying microservices
Transitioning from a monolithic architecture to microservices demands strategic foresight and meticulous execution. Effective testing at various levels—unit, contract, integration, and end-to-end—is essential. Strategies like blue-green deployment, canary deployment, rolling updates, and serverless deployment minimize issues and downtime during deployment. Netflix's approach involves stress testing, chaos engineering, and continuous integration and deployment to ensure resilience and fault tolerance.
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Article
Community Picks·2y
How Netflix Uses Chaos Engineering to Create Resilience Systems 🐒
Netflix transformed from a DVD rental service to a streaming giant using chaos engineering to build resilient distributed systems. By proactively finding and fixing potential failures through controlled experiments and automation, such as using tools like Chaos Monkey, Netflix ensures minimal downtime and high system availability. Key principles include running tests in production, automating fixes, and controlling the test's blast radius to prevent user impact.
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Article
Netflix TechBlog·1y
Title Launch Observability at Netflix Scale
Netflix manages over a thousand global content launches each month and faces significant challenges in ensuring the success and discoverability of each title. This post discusses the operational demands of a personalization system, highlighting the need for scalable solutions to automate operations. Two primary options are explored: log processing and observability endpoints, each with its benefits and tradeoffs. Real-time monitoring and proactive issue detection are key strategies in enhancing Netflix's ability to manage title launches effectively.
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Article
GamesIndustry.biz·2y
"I just dropped them an email" – How a small studio won over Netflix to make a Queen's Gambit game
Jamie Brayshaw from Ripstone shared insights at Develop Brighton on how the studio successfully pitched and developed a Queen's Gambit game for Netflix. Despite tight deadlines, the studio leveraged its chess game expertise and bold pitching strategy. The project faced numerous challenges, including adapting Unreal Engine for mobile, addressing a diverse audience, and ensuring smooth first-time user experience. Continuous user testing was crucial in refining the game, which includes bite-sized puzzles alongside traditional chess gameplay.
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Article
Quastor Daily·2y
How Netflix Implements Load Shedding
Netflix implements load shedding in their API Gateway to prioritize critical requests and ensure a smooth streaming experience for users.
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Article
ByteByteGo·2y
Evolution of Java Usage at Netflix
The post discusses the evolution of Java usage at Netflix, from the Groovy era with BFFs to the move to GraphQL Federation. It also mentions the use of Java versions and Spring Boot at Netflix.
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Article
InfoQ·2y
Building a Global Caching System at Netflix: A Deep Dive to Global Replication
Netflix uses a global replication strategy with EVCache, a distributed key-value store, to ensure data availability across four regions. EVCache handles 30 million global replication events and 400 million operations per second, leveraging 200 Memcached clusters and 22,000 servers. Features include client-initiated replication, topology-aware clients, and batch compression, which reduce network costs and enhance performance. The replication process involves client-initiated data mutations, Kafka for metadata handling, and SQS for robust error handling.
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Article
Data Engineer Things·1y
I spent 4 hours learning how Netflix operates Apache Iceberg at scale.
Netflix has developed a sophisticated data platform to handle extensive data pipelines and analytics, using Apache Iceberg to overcome the limitations of their previous Hive-based system. Key components include Polaris, a custom metastore for Iceberg, and Janitors, a cleanup service. They also implemented Autotune for optimizing data layout and Autolift for localizing data files. Moreover, secure access controls were established for Iceberg tables. Netflix's migration tool for transitioning from Hive to Iceberg minimizes data movement and business interruptions.
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Video
Community Picks·2y
Vim As Your Editor - Introduction
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Article
Netflix TechBlog·1y
Netflix’s Distributed Counter Abstraction
Netflix's Distributed Counter Abstraction is a high-performance, scalable counting service built on top of their TimeSeries Abstraction. It supports two primary counting modes—Best-Effort and Eventually Consistent—to cater to different use cases and trade-offs involving accuracy, latency, and infrastructure costs. The service aims to handle high throughput and availability by leveraging a combination of caching, durable queuing, and periodic aggregation mechanisms. Additionally, it incorporates various approaches to manage potential data loss, idempotency, and contention issues inherent in distributed systems.
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Article
ByteByteGo·2y
Netflix: What Happens When You Press Play?
Netflix is a global streaming service with millions of subscribers and a large budget for creating new content. It operates in two clouds, AWS and Open Connect, and is divided into three parts: the client, the backend, and the content delivery network (CDN). Netflix moved to AWS in 2008 to improve reliability and scalability.
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Article
Netflix TechBlog·2y
Introducing Netflix TimeSeries Data Abstraction Layer
Netflix has introduced a TimeSeries Data Abstraction Layer designed to handle vast amounts of temporal data with millisecond access latency. Key features include efficient data partitioning, flexible storage integration (using Apache Cassandra and Elasticsearch), and scalability to manage high-throughput, immutable temporal event data. This abstraction layer optimizes storage and query efficiency, addressing issues like global read/write operations, tunable configurations, bursty traffic management, and cost efficiency. It plays a vital role in various Netflix services like user interaction tracking, feature rollout analysis, and asset impression tracking.
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Article
Quastor Daily·2y
How Netflix Implements Load Shedding
Learn how Netflix implements load shedding to ensure a smooth user experience during high usage periods.
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Article
Data Engineer Things·1y
How does Netflix ensure the data quality for thousands of Apache Iceberg tables?
Netflix employs the Write-Audit-Publish (WAP) pattern using Apache Iceberg to maintain high data quality across thousands of tables. The WAP pattern involves writing data to a hidden snapshot, auditing it, and publishing it only if it passes quality checks. This approach is analogous to CI/CD workflows, ensuring validated data is exposed to downstream consumers. Apache Iceberg's structure, including manifest files, metadata files, and catalog, supports efficient snapshot management and branching, similar to version control in Git.
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Article
Netflix TechBlog·2y
Enhancing Netflix Reliability with Service-Level Prioritized Load Shedding
Netflix has enhanced its reliability by extending prioritized load shedding techniques from the API gateway level to the individual service level, specifically for the video streaming control plane. This approach prioritizes critical user-initiated requests over non-critical pre-fetch requests, using partitioned concurrency limiters. This strategy proved effective during high-traffic incidents, ensuring high availability for user-initiated requests. Netflix also developed an internal library for prioritized load shedding using predefined priority buckets and incorporated CPU and IO-based load shedding techniques to maintain system performance under stress.
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Article
Watercooler·2y
Netflix's Hello World
Netflix's Hello World is a programming concept with significance and can be accessed on Netflix's website.
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Article
All Things Open·2y
How Netflix uses an innovative approach to technical debt
Lisa Smith, an engineering manager at Netflix, discusses an innovative approach to handling technical debt by viewing it as an opportunity to make better decisions and encourage innovation. She also emphasizes the importance of including diverse voices in the tech community and suggests using tools like JIRA to track and measure technical debt effectively.
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Article
ByteByteGo·2y
Open Connect Appliances
Netflix has developed Open Connect Appliances (OCAs) for video storage. These OCAs are fast servers with large storage capacity. They use commodity PC components and the FreeBSD operating system. Netflix places OCAs in ISP data centers and internet exchange locations to build its CDN.
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Article
The New Stack·1y
Netflix Engineers Rethink Mock Testing for GraphQL
Netflix engineers are reevaluating mock testing strategies for GraphQL to enhance production reliability. Creating effective mocks for its complex infrastructure poses significant challenges. Traditional UI testing lacks comprehensiveness for distributed environments, while canary releases and integration testing offer more reliability. An ideal testing solution should realistically model all traffic without disrupting development workflows. Netflix's new approach leverages its DGS framework for customizable and user-friendly mock testing, although it's still in development. Collaboration and understanding diverse team needs are key to success.
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