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Netflix
Subscription-based streaming service that lets you watch TV shows, movies, and documentaries over the internet.
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Netflix ·
A Tale of Two Flink Autoscalers
Netflix operates over 30,000 Apache Flink jobs across multiple AWS regions, ranging from managed pipelines to complex stateful DAGs. Its initial in-house autoscaler used external telemetry from Mantis and Atlas to scale TaskManagers as a single unit, cutting resource usage by 25–45% but failing on multi-operator stateful topologies. To address these limitations, Netflix adopted the Apache Flink Autoscaler library, which calculates True Processing Rate and per-vertex parallelism using internal metrics. The architecture uses Temporal workflows orchestrated within a Spring Boot application to evaluate jobs individually and prevent cross-job blast radiuses. Implementing the open-source autoscaler reduced annualized compute expenditures by 58% for client telemetry and logging, saving approximately $1.1 million.
Netflix Technology BlogNetflix ·
How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC…
Netflix designed a query execution serving layer for its Real-Time Distributed Graph to power sub-100ms responses across diverse graph traversal workloads spanning billions of nodes and edges. To prevent compounding network delays in multi-hop queries, the engine uses a breadth-first traversal model that batches entity lookups across entire frontier levels rather than tracing paths depth-first. The entire serving architecture relies on asynchronous composition across small thread pools of 16 to 24 threads, ensuring no thread blocks while waiting on remote storage or enrichment I/O. Selective caching via EVCache targets stable properties with volatility-matched TTLs, producing 70 to 80 percent cache hit rates and reducing backend storage calls by three to four times. A layered filtering hierarchy pushes depth and edge limits directly to the storage tier, eliminating bespoke code changes while isolating system resources against excessive fan-out.
Netflix Technology BlogNetflix ·
Modeling Device Capabilities for Analytics
Netflix supports diverse features, such as 4K streaming, immersive audio, and cloud gaming, across varied hardware platforms. Because hardware constraints like RAM, CPU cores, and display capabilities limit feature support on certain models, understanding device capabilities is crucial for managing user experiences. To address this challenge, Netflix built a comprehensive device capability data model integrated with internal feature flags. The architecture employs a cumulative table to capture the latest state of device capabilities alongside a histogram table tracking 28-day active device counts grouped by model and software version. These datasets power analytical products that evaluate feature reach and inform rollout decisions for capabilities like Netflix Spatial Audio and 4K Ultra HD.
Netflix Technology BlogNetflix ·
GenRec: Towards LLM-Native Recommendation at Netflix
Netflix developed GenRec, an LLM-backed recommendation ranker built by adapting an internal foundation model for large-scale personalization. Traditional recommendation stacks rely on thousands of hand-crafted features and specialized architectures that are costly to maintain across diverse content types. To replace manual feature pipelines, GenRec verbalizes user histories, metadata, and contexts into natural-language prompts and trains with multi-objective losses, including catalog-aware ranking and reward-weighted alignment. At inference time, the model executes in prefill-only mode on vLLM without decoding text. In large-scale online A/B testing against a mature production ranker, GenRec achieved statistically significant improvements in short-term and long-term metrics while using fewer labeled examples.
Netflix Technology BlogNetflix ·
In-House LLM Serving at Netflix
Netflix established an in-house serving platform to run large language model inference directly inside existing production environments alongside traditional machine learning models. Built upon NVIDIA Triton Inference Server and vLLM, the unified architecture handles member-scale routing, candidate generation, feature fetching, and model execution over gRPC and OpenAI-compatible HTTP endpoints. The team shifted their primary engine from TensorRT-LLM to vLLM to support custom architectures, simpler debugging, and non-trivial constraint logic. Addressing production obstacles required patching Triton's frontend for guided decoding, pinning dependent library versions, rewriting logits processors in C++ for vLLM V1, and handling state machine resets during engine preemptions. The resulting platform unifies deployment flows while preserving operational stability across evolving model schemas.
Netflix Technology BlogNetflix ·
Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned
Engineers at Netflix required a unified, real-time view of service dependencies to navigate distributed architecture and improve incident troubleshooting. Traditional batch systems introduced stale data, so the team created a streaming-first platform backed by reactive streams and backpressure handling to ingest flow records from multi-region Kafka streams and Server-Sent Events without data loss. The architecture partitions data into physically separate graph and columnar storage layers covering eBPF network flows, IPC metrics, and distributed traces. Network flow ingestion relies on a three-stage distributed aggregation pipeline using consistent hashing to resolve network intermediaries into logical application connections. The resulting production system serves time-travel and topology queries with sub-second latency while continuously updating dependency views.
Netflix Technology BlogNetflix ·
GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Netflix traditionally constructs its structured two-dimensional homepage through a complex, multi-stage recommender pipeline that separates candidate generation and ranking across rows and entities. To simplify this architecture and optimize directly for whole-page user satisfaction, Netflix developed GenPage, an end-to-end generative transformer model that autoregressively builds the entire homepage from raw tokenized context. The system relies on a domain-specific tokenizer to compress engagement history, context injection for cold start, hybrid row decoding to minimize decoding steps, and reinforcement learning post-training. In online A/B testing against the production baseline, GenPage delivered statistically significant gains in core user engagement metrics while cutting end-to-end serving latency by 20 percent. Offline evaluations further showed that enriching context prompts improved recommendation quality more effectively than increasing model capacity in the current operating regime.
Netflix Technology BlogNetflix ·
Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix
Generative video editing tools often regenerate entire video clips, which inadvertently modifies untouched scene details or violates physical continuity during object deletion. To provide artists with precise control, two research prototypes were developed: Vera and VOID. Vera uses a layered video diffusion framework with a Mixture-of-Transformers architecture to jointly generate separate edit and alpha matte layers, compositing them with original footage while leaving unchanged pixels untouched. VOID addresses object deletion by conditioning on interaction-aware quadmasks and using a second-pass appearance refiner to reconstruct scenes with plausible physical continuity. In a user study evaluated across 75 real-world scenarios, reviewers selected VOID 64.8% of the time over six baselines.
Netflix Technology BlogNetflix ·
How Netflix Simplified Batch Compute with Kueue
Netflix transitioned its managed batch compute infrastructure from a homegrown solution called Compute Managed Batch to Kueue on its Titus container platform. CMB previously relied on custom scheduling and admission-only fair sharing without preemption, making feature development cumbersome as the Kubernetes ecosystem evolved. To modernize the platform transparently, Netflix mapped internal tenants to Cohorts and leaf tenants to ClusterQueues and LocalQueues while routing jobs through a custom Kueue router. Kueue operates alongside existing Titus scheduling profiles rather than replacing the kube-scheduler, preserving cluster placement efficiency. The migration was completed in four weeks across millions of batch workloads, significantly increasing average resource utilization through preemption-based fair sharing.
Netflix Technology BlogNetflix ·
The Data Canary: How Netflix Validates Catalog Metadata
A manual mitigation action during an incident corrupted a data feed for a subset of titles, causing playback issues and catalog service failures that existing code canary systems failed to catch. To protect streaming reliability, Netflix built an automated data canary system that validates transformed catalog metadata prior to publication. The architecture utilizes a dedicated orchestrator alongside permanent baseline and canary service clusters to coordinate validation using real production traffic. By leveraging custom chaos experiment thresholds, sticky session affinity, and Starts Per Second playback metrics, the system detects regressions in under ten minutes and blocks publication automatically. Controlled failure injection experiments routing approximately 0.2% of global traffic confirmed that issues could be identified in 2.5 to 4 minutes.
Netflix Technology Blog