A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases, managing thousands of unique Metaflow flows. This followed a previous blog on the same topic. Many of these projects are under constant development by dedicated teams with their own business goals and development best practices, such as the system that supports our content decision makers, or the system that ranks which language subtitles are most valuable for a specific piece of content.

As a central ML and AI platform team, our role is to empower our partner teams with tools that maximize their productivity and effectiveness, while adapting to their specific needs (not the other way around). This has been a guiding design principle with Metaflow since its inception.

At Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members. To achieve this, we are committed to building robust systems that deliver comprehensive observability, enabling us to take full accountability for every title on our service.

At Netflix, we use Amazon Web Services (AWS) for our cloud infrastructure needs, such as compute, storage, and networking to build and run the streaming platform that we love. Our ecosystem enables engineering teams to run applications and services at scale, utilizing a mix of open-source and proprietary solutions. In turn, our self-serve platforms allow teams to create and deploy, sometimes custom, workloads more efficiently. This diverse technological landscape generates extensive and rich data from various infrastructure entities, from which, data engineers and analysts collaborate to provide actionable insights to the engineering organization in a continuous feedback loop that ultimately enhances the business.

One crucial way in which we do this is through the democratization of highly curated data sources that sunshine usage and cost patterns across Netflix’s services and teams. The Data & Insights organization partners closely with our engineering teams to share key efficiency metrics, empowering internal stakeholders to make informed business decisions.

In our previous blog post, we introduced Netflix’s TimeSeries Abstraction, a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction. This counting service, built on top of the TimeSeries Abstraction, enables distributed counting at scale while maintaining similar low latency performance. As with all our abstractions, we use our Data Gateway Control Plane to shard, configure, and deploy this service globally.

Distributed counting is a challenging problem in computer science. In this blog post, we’ll explore the diverse counting requirements at Netflix, the challenges of achieving accurate counts in near real-time, and the rationale behind our chosen approach, including the necessary trade-offs.

Note: When it comes to distributed counters, terms such as ‘accurate’ or ‘precise’ should be taken with a grain of salt. In this context, they refer to a count very close to accurate, presented with minimal delays.

At Netflix, the Analytics and Developer Experience organization, part of the Data Platform, offers a product called Workbench. Workbench is a remote development workspace based on Titus that allows data practitioners to work with big data and machine learning use cases at scale. A common use case for Workbench is running JupyterLab Notebooks.

Recently, several users reported that their JupyterLab UI becomes slow and unresponsive when running certain notebooks. This document details the intriguing process of debugging this issue, all the way from the UI down to the Linux kernel.

As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming, the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital. In previous blog posts, we introduced the Key-Value Data Abstraction Layer and the Data Gateway Platform, both of which are integral to Netflix’s data architecture. The Key-Value Abstraction offers a flexible, scalable solution for storing and accessing structured key-value data, while the Data Gateway Platform provides essential infrastructure for protecting, configuring, and deploying the data tier.

Building on these foundational abstractions, we developed the TimeSeries Abstraction — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases.

In this post, we dive deep into how Netflix’s KV abstraction works, the architectural principles guiding its design, the challenges we faced in scaling diverse use cases, and the technical innovations that have allowed us to achieve the performance and reliability required by Netflix’s global operations.

The Compute and Performance Engineering teams at Netflix regularly investigate performance issues in our multi-tenant environment. The first step is determining whether the problem originates from the application or the underlying infrastructure. One issue that often complicates this process is the "noisy neighbor" problem. On Titus, our multi-tenant compute platform, a "noisy neighbor" refers to a container or system service that heavily utilizes the server's resources, causing performance degradation in adjacent containers. We usually focus on CPU utilization because it is our workloads’ most frequent source of noisy neighbor issues.

Detecting the effects of noisy neighbors is complex. Traditional performance analysis tools such as perf can introduce significant overhead, risking further performance degradation. Additionally, these tools are typically deployed after the fact, which is too late for effective investigation. Another challenge is that debugging noisy neighbor issues requires significant low-level expertise and specialized tooling. In this blog post, we'll reveal how we leveraged eBPF to achieve continuous, low-overhead instrumentation of the Linux scheduler, enabling effective self-serve monitoring of noisy neighbor issues. You’ll learn how Linux kernel instrumentation can improve your infrastructure observability with deeper insights and enhanced monitoring.

Pushy is Netflix’s WebSocket server that maintains persistent WebSocket connections with devices running the Netflix application. This allows data to be sent to the device from backend services on demand, without the need for continually polling requests from the device. Over the last few years, Pushy has seen tremendous growth, evolving from its role as a best-effort message delivery service to be an integral part of the Netflix ecosystem. This post describes how we’ve grown and scaled Pushy to meet its new and future needs, as it handles hundreds of millions of concurrent WebSocket connections, delivers hundreds of thousands of messages per second, and maintains a steady 99.999% message delivery reliability rate.

Our mission at Netflix is to entertain the world. Our personalization algorithms play a crucial role in delivering on this mission for all members by recommending the right shows, movies, and games at the right time. This goal extends beyond immediate engagement; we aim to create an experience that brings lasting enjoyment to our members. Traditional recommender systems often optimize for short-term metrics like clicks or engagement, which may not fully capture long-term satisfaction. We strive to recommend content that not only engages members in the moment but also enhances their long-term satisfaction, which increases the value they get from Netflix, and thus they’ll be more likely to continue to be a member.

By Aurélien Bibaut, Winston Chou, Simon Ejdemyr, and Nathan Kallus

Netflix operates a highly efficient cloud computing infrastructure that supports a wide array of applications essential for our SVOD (Subscription Video on Demand), live streaming and gaming services. Utilizing Amazon AWS, our infrastructure is hosted across multiple geographic regions worldwide. This global distribution allows our applications to deliver content more effectively by serving traffic closer to our customers. Like any distributed system, our applications occasionally require data synchronization between regions to maintain seamless service delivery.

Getting real with virtual threads.

Maestro is a general-purpose, horizontally scalable workflow orchestrator designed to manage large-scale workflows such as data pipelines and machine learning model training pipelines. It oversees the entire lifecycle of a workflow, from start to finish, including retries, queuing, task distribution to compute engines, etc.. Users can package their business logic in various formats such as Docker images, notebooks, bash script, SQL, Python, and more. Unlike traditional workflow orchestrators that only support Directed Acyclic Graphs (DAGs), Maestro supports both acyclic and cyclic workflows and also includes multiple reusable patterns, including foreach loops, subworkflow, and conditional branch, etc.

Applying Quality of Service techniques at the application level.

这篇文章介绍了一个名为Video Annotator (VA)的交互式框架，用于标注视频数据。VA利用大型视觉语言模型的零样本能力和主动学习技术，提高了样本效率和降低成本。它提供了一种独特的方法来标注、管理和迭代视频分类数据集，强调领域专家在人机交互系统中的直接参与。通过在标注过程中让用户快速做出决策，VA提高了系统的整体效率。它还支持持续的标注过程，用户可以快速部署模型、监控质量并迅速修复问题。这种自助式架构赋予领域专家在不需要数据科学家或第三方标注者的参与下进行改进的能力，建立了对系统的信任。经过实验，VA在多个视频理解任务中相对于竞争对手平均提高了8.3个平均精度点。他们还发布了一个包含153k标签的数据集和复制实验的代码。