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Introducing Netflix TimeSeries Data Abstraction Layer

By Rajiv Shringi, Vinay Chella, Kaidan Fullerton, Oleksii Tkachuk, Joey LynchIntroductionAs 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...

Tue Oct 8, 2024 20:15
Introducing Netflix’s Key-Value Data Abstraction Layer

Vidhya Arvind, Rajasekhar Ummadisetty, Joey Lynch, Vinay ChellaIntroductionAt Netflix our ability to deliver seamless, high-quality, streaming experiences to millions of users hinges on robust, global backend infrastructure. Central to this infrastructure is our use of multiple online distributed databases such as Apache Cassandra, a NoSQL database...

Thu Sep 19, 2024 01:48
Pushy to the Limit: Evolving Netflix’s WebSocket proxy for the future

By Karthik Yagna, Baskar Odayarkoil, and Alex EllisPushy 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...

Tue Sep 10, 2024 22:19
Noisy Neighbor Detection with eBPF

By Jose Fernandez, Sebastien Dabdoub, Jason Koch, Artem TkachukThe 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...

Tue Sep 10, 2024 20:59
Recommending for Long-Term Member Satisfaction at Netflix

By Jiangwei Pan, Gary Tang, Henry Wang, and Justin BasilicoIntroductionOur 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...

Thu Aug 29, 2024 04:01
Improve Your Next Experiment by Learning Better Proxy Metrics From Past Experiments

By Aurélien Bibaut, Winston Chou, Simon Ejdemyr, and Nathan KallusWe are excited to share our work on how to learn good proxy metrics from historical experiments at KDD 2024. This work addresses a fundamental question for technology companies and academic researchers alike: how do we establish that a treatment that improves short-term (statistically...

Mon Aug 26, 2024 18:51

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