Best of Netflix — 2025

  1. 1
    Article
    Avatar of techworld-with-milanTech World With Milan·1y

    How does Netflix manage to show you a movie without interruptions?

    Netflix delivers buffer-free streaming through a sophisticated distributed systems architecture. The platform uses Amazon Web Services for managing control-plane operations and its custom Content Delivery Network, Open Connect, to handle data-plane operations. Key components include hundreds of microservices, a two-tier CDN deployment, adaptive bitrate streaming, and advanced resilience engineering practices. This setup allows for smooth content delivery and high availability, even under heavy load.

  2. 2
    Video
    Avatar of codeheadCodeHead·49w

    Wait, Netflix is... Java?!

    Netflix leverages Java for its backend, despite its reputation as outdated or verbose. Java's performance, scalability, and mature ecosystem make it a reliable choice for Netflix's microservices. With tools like Spring Boot, Netflix efficiently manages its vast streaming services. The use of Java demonstrates the company's preference for dependable technology over trends.

  3. 3
    Article
    Avatar of bytebytegoByteByteGo·46w

    How Netflix Runs on Java?

    Netflix operates its massive streaming platform primarily on Java, utilizing a federated GraphQL architecture with Spring Boot microservices. The company migrated from Java 8 to JDK 21+, adopting virtual threads for improved concurrency and ZGC garbage collector for near-zero pause times. Their backend consists of around 3000 Spring Boot services communicating via gRPC, with GraphQL serving as the client-facing API layer. Netflix moved away from reactive programming (RxJava) in favor of virtual threads and structured concurrency, while building custom tooling to maintain their Spring Boot Netflix stack with company-specific integrations for security, observability, and service mesh functionality.

  4. 4
    Article
    Avatar of bytebytegoByteByteGo·1y

    How Netflix Stores 140 Million Hours of Viewing Data Per Day

    Netflix handles millions of hours of viewing data daily by using Apache Cassandra for flexible, scalable data storage. The system has evolved to manage the increasing volume and complexity of data, implementing strategies such as horizontal partitioning, compressed storage for older data, and efficient data retrieval methods. To further optimize performance and reduce costs, Netflix redesigned its architecture to categorize data by type and age, improving both storage efficiency and retrieval speeds.

  5. 5
    Article
    Avatar of bytebytegoByteByteGo·1y

    How Netflix Orchestrates Millions of Workflow Jobs with Maestro

    Netflix transitioned from using the Meson orchestrator to Maestro due to scalability issues with the growing volume of data and workflows. Maestro, built with a distributed microservices architecture, efficiently manages large-scale workflows with high reliability and low operational overhead. It supports dynamic workflows, defined via DSLs, a visual UI, or programmatic APIs, and leverages technologies such as CockroachDB and distributed queues. Features like event publishing, parameterized workflows, and an integrated signal service enable Maestro to handle extensive data processing and machine learning tasks at scale.

  6. 6
    Article
    Avatar of netflixNetflix TechBlog·41w

    Netflix Tudum Architecture: from CQRS with Kafka to CQRS with RAW Hollow

    Netflix migrated their Tudum fan site architecture from a CQRS pattern using Kafka and traditional caching to RAW Hollow, an in-memory compressed object database. The original architecture suffered from eventual consistency delays, taking minutes for content changes to appear. RAW Hollow eliminated the need for separate databases and Kafka infrastructure by storing the entire dataset in memory across application processes, reducing homepage construction time from 1.4 seconds to 0.4 seconds and enabling real-time content previews.

  7. 7
    Article
    Avatar of netflixNetflix TechBlog·30w

    Building a Resilient Data Platform with Write-Ahead Log at Netflix

    Netflix built a generic Write-Ahead Log (WAL) system to solve data consistency and reliability challenges at scale. The system provides a simple API that abstracts underlying message queues (Kafka, SQS) and supports multiple use cases including delayed queues, cross-region replication, and multi-partition mutations. WAL prevents data loss, handles system entropy across different datastores, and enables reliable retry mechanisms for real-time data pipelines. The architecture separates message producers from consumers, uses configurable namespaces for logical separation, and leverages Netflix's Data Gateway infrastructure for deployment. Key applications include EVCache cross-region replication, Live Origin's delayed delete operations, and Key-Value service's MutateItems API with two-phase commit semantics.

  8. 8
    Article
    Avatar of bytebytegoByteByteGo·32w

    How Netflix Tudum Supports 20 Million Users With CQRS

    Netflix redesigned their Tudum platform architecture to support 20 million users by replacing a traditional CQRS implementation with RAW Hollow, an in-memory object store. The original design used Kafka and Cassandra with caching layers, causing delays in editorial previews due to eventual consistency. By embedding RAW Hollow directly into microservices, they eliminated external datastores and reduced page construction time from 1.4 seconds to 0.4 seconds while enabling near-instant editorial previews. The compressed in-memory approach stores three years of data in just 130MB while maintaining strong consistency options for critical workflows.

  9. 9
    Article
    Avatar of bytebytegoByteByteGo·20w

    How Netflix Built a Distributed Write Ahead Log For Its Data Platform

    Netflix built a distributed Write-Ahead Log (WAL) system to solve data reliability issues across their platform. The WAL captures every data change before applying it to databases, enabling automatic retries, cross-region replication, and multi-partition consistency. Built on top of their Data Gateway Infrastructure, it uses Kafka and Amazon SQS as pluggable backends, supports multiple use cases through namespaces, and scales independently through sharded deployments. The system provides durability guarantees while allowing teams to configure retry logic, delays, and targets without code changes.

  10. 10
    Article
    Avatar of programmingdigestProgramming Digest·36w

    Inside Netflix’s $1 Billion Algorithm - How Recommendations Predict Your Next Binge

    Netflix's recommendation algorithm uses matrix factorization and collaborative filtering to analyze user behavior and predict preferences, saving the company over $1 billion annually. The system breaks down sparse user-item rating matrices into dense feature matrices that capture hidden patterns in viewing habits. The article explains the mathematical concepts behind recommendations, provides Python code examples for building a basic recommender system, and covers advanced techniques like neural collaborative filtering and real-time learning systems that adapt to changing user preferences.

  11. 11
    Article
    Avatar of netflixNetflix TechBlog·50w

    Behind the Scenes: Building a Robust Ads Event Processing Pipeline

    Netflix developed a robust ads event processing pipeline to enhance digital advertising strategies. The system includes components for ad insertion, tracking, and real-time feedback to optimize ad delivery and ensure accurate reporting. Netflix's approach addresses scalability and integration with third-party vendors, leveraging technologies like Apache Kafka and Flink for data processing. The evolution into an in-house advertising platform refines capabilities like frequency capping and sessionization, improving reporting and metrics, and supporting future ad types and strategies.

  12. 12
    Article
    Avatar of netflixNetflix TechBlog·31w

    Empowering Netflix Engineers with Incident Management

    Netflix transformed their incident management from a centralized SRE-only process to a democratized approach where all engineering teams can declare and manage incidents. They adopted Incident.io as their platform, focusing on intuitive design, internal data integration, balanced customization, and organizational investment in training. This shift resulted in 50% adoption across engineering teams within six months and fostered a culture where incidents are viewed as learning opportunities rather than scary outages.

  13. 13
    Article
    Avatar of bytebytegoByteByteGo·1y

    How Netflix Built a Distributed Counter for Billions of User Interactions

    Netflix uses a Distributed Counter Abstraction to efficiently track billions of user interactions. This system addresses the need for low latency, high throughput, and cost efficiency by utilizing different counting techniques tailored to various use cases. The architecture employs a hybrid approach combining event logging, background aggregation, and caching. Key benefits include scalability, reliability, and balancing trade-offs between immediacy and consistency.

  14. 14
    Article
    Avatar of clickhouseClickHouse·26w

    How Netflix optimized its petabyte-scale logging system with ClickHouse

    Netflix processes 5 petabytes of logs daily using ClickHouse, handling 10.6 million events per second with sub-second query performance. Three key optimizations enabled this scale: replacing regex-based log fingerprinting with generated lexers (8-10x faster), implementing custom native protocol serialization for efficient data ingestion, and sharding tag maps to reduce query times from 3 seconds to 700ms. The system combines ClickHouse for hot data with Apache Iceberg for long-term storage, making logs searchable within 20 seconds while serving 500-1,000 queries per second across 40,000+ microservices.

  15. 15
    Article
    Avatar of baeldungBaeldung·19w

    Introduction to Netflix Hollow

    Netflix Hollow is a low-latency Java framework for distributing data from a source to multiple targets using a producer-consumer model. The producer fetches data from external systems and publishes snapshots to file systems or object storage, while consumers read and process these snapshots. The framework efficiently manages memory by offloading large datasets to external storage, addressing Java heap space issues. Implementation involves defining entity classes with primary keys, setting up publishers and announcers for producers, generating consumer APIs using HollowAPIGenerator, and configuring announcement watchers and retrievers for consumers. The library handles snapshot versioning, updates, and notifications automatically.

  16. 16
    Video
    Avatar of hitenshowHiten Shah·31w

    Meet Showrunner: The AI startup that wants to replace Netflix

    Showrunner AI is positioning itself as a potential Netflix disruptor by enabling users to create personalized TV shows and episodes using AI. The platform allows people to write prompts, cast characters, and generate animated content, moving beyond passive consumption to active creation. While current limitations include short animated episodes and inconsistent quality, the technology mirrors Netflix's early scrappy beginnings. The startup has already gained traction with viral AI-generated South Park episodes and has 100,000 people on their waitlist, with backing from Amazon and licensing discussions with Disney.

  17. 17
    Video
    Avatar of primeagenThePrimeTime·29w

    day in the life at netflix

    A critical commentary analyzing a Netflix employee's day-in-the-life video, breaking down the actual work time versus non-work activities. The analysis reveals approximately 2 hours of desk work and 1 hour of meetings across a 5-hour office day, with the remainder spent on meals and socializing. The commentary contrasts this work style with a more creation-focused approach to software development, questioning the value of minimal productivity and discussing implications for job security in an AI-driven future.

  18. 18
    Article
    Avatar of netflixNetflix TechBlog·18w

    How Temporal Powers Reliable Cloud Operations at Netflix

    Netflix reduced transient deployment failures from 4% to 0.0001% by migrating cloud operation orchestration from Spinnaker's homegrown system to Temporal's durable execution platform. The original Clouddriver service suffered from complex internal orchestration, instance-local state, and unreliable retry logic. By implementing cloud operations as Temporal workflows with activities, Netflix eliminated tight coupling between services, removed thousands of lines of custom orchestration code, and gained automatic retries, state persistence, and better observability. The migration used abstraction layers and dynamic configuration to transparently onboard all applications within two quarters.

  19. 19
    Article
    Avatar of stackovStack Overflow Blog·50w

    Mastering microservices with a former Uber and Netflix architect

    Jeu, a former architect at Uber and Netflix, now cofounder of Orkes, shares insights on mastering microservices. Orkes offers a developer-first enterprise workflow orchestration platform. The post highlights contributions by Stack Overflow user Alex Stiff.

  20. 20
    Article
    Avatar of netflixNetflix TechBlog·31w

    Scaling Muse: How Netflix Powers Data-Driven Creative Insights at Trillion-Row Scale

    Netflix evolved their Muse analytics platform to handle trillion-row datasets by implementing HyperLogLog sketches for approximate distinct counts, using Hollow for in-memory precomputed aggregates, and extensively tuning their Apache Druid cluster. The migration reduced query latencies by 50% while supporting advanced filtering capabilities for creative content insights. The team used parallel stack deployment, automated validation, and granular feature flags to ensure data accuracy during the transition.

  21. 21
    Article
    Avatar of codemotionCodemotion·44w

    How Netflix Scales to 270 Million Users with Java and Microservices

    Netflix serves 270 million users through a sophisticated microservices architecture built primarily with Java. The platform splits operations between a control plane on AWS handling user interactions and recommendations, and a proprietary CDN called Open Connect with 17,000+ servers worldwide for content delivery. Key innovations include circuit breaker patterns with Hystrix, service discovery with Eureka, reactive programming with RxJava, and chaos engineering practices. The architecture employs polyglot persistence across multiple databases, extensive observability with petabytes of telemetry data, and hundreds of machine learning models for personalized recommendations.

  22. 22
    Article
    Avatar of netflixNetflix TechBlog·29w

    100X Faster: How We Supercharged Netflix Maestro’s Workflow Engine

    Netflix redesigned their Maestro workflow orchestrator engine, achieving 100x performance improvement by replacing the stateless worker model with a stateful actor-based architecture using Java virtual threads. The new design reduces overhead from seconds to milliseconds, maintains in-memory state for better locality, implements strong execution guarantees, and simplifies the architecture by removing dependencies on external distributed queues and multiple databases.

  23. 23
    Article
    Avatar of detlifeData Engineer Things·1y

    Netflix Movie Analytics (Homemade)

    A data engineer combines a passion for film with data analytics by analyzing their Netflix viewing habits. Using data exported from Netflix and enriched through The Movie Database (TMDB) API, they store and process the data on Google Cloud Platform (GCP). The data is modeled into a Star Schema on Google BigQuery, orchestrated with Airflow, and visualized using Tableau. Key insights include favorite genres, preferred viewing days, and overall streaming patterns.

  24. 24
    Article
    Avatar of netflixNetflix TechBlog·40w

    Behind the Streams: Three Years Of Live at Netflix. Part 1.

    Netflix built a comprehensive live streaming architecture over three years, handling events from comedy specials to NFL games and boxing matches. The system leverages dedicated broadcast facilities, cloud-based transcoding pipelines using AWS MediaConnect and MediaLive, the Open Connect CDN for global delivery, and HTTPS-based streaming with AVC/HEVC codecs. Key learnings include the importance of extensive testing, regular practice events, viewership prediction, graceful degradation strategies, and comprehensive contingency planning with dedicated launch rooms and game day exercises.

  25. 25
    Video
    Avatar of codingwithlewisCoding with Lewis·42w

    3 Insane Algorithms Netflix Uses to Scan BILLIONS of Frames

    Netflix uses three sophisticated computer vision algorithms to analyze billions of video frames: match cut transitions that automatically find visually similar shots for seamless editing, video search capabilities that convert text queries into mathematical embeddings to find specific scenes, and scene detection that combines screenplay alignment with multimodal analysis of video and audio tracks. These systems leverage instance segmentation, optical flow, and bidirectional neural networks to automate video editing tasks that would otherwise require thousands of manual hours.

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