Remove Cache Remove Development Remove Traffic Remove Tuning
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Improving our video encodes for legacy devices

The Netflix TechBlog

264/AVC Main profile family still represents a substantial portion of the members viewing hours and an even larger portion of the traffic. Further tuning of pre-defined encoding parameters. which we originally used as a quality measure, before we developed VMAF. Yet, given its wide support, our H.264/AVC

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Meet Hydrogen: A React Framework For Dynamic, Contextual And Personalized E-Commerce

Smashing Magazine

As developers, we rightfully obsess about the customer experience, relentlessly working to squeeze every millisecond out of the critical rendering path, optimize input latency, and eliminate jank. On top of this foundation, we add layers of caching, prerendering and edge delivery optimizations — not the other way around.

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Delta: A Data Synchronization and Enrichment Platform

The Netflix TechBlog

caching (Memcached etc.), To address these challenges, we developed Delta. Delta Delta has been developed to address the limitations of existing solutions for data synchronization, and also allows to enrich data on the fly. Please stay tuned. providing advanced search capabilities (ElasticSearch etc.),

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Cache and Prizes

Alex Russell

Browsers will cache tools popular among vocal, leading-edge developers. There's plenty of space for caching most popular frameworks. Developers won't need to do work to realise a benefit. The best available proxy data also suggests that shared caches would have a minimal positive effect on performance.

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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The GraphQL shim enabled client engineers to move quickly onto GraphQL, figure out client-side concerns like cache normalization, experiment with different GraphQL clients, and investigate client performance without being blocked by server-side migrations. The Replay Tester tool samples raw traffic streams from Mantis.

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Firstly, developers struggled to reason about consistency, durability and performance in this complex global deployment across multiple stores. Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns.

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ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning

The Netflix TechBlog

The team has also developed a few common attribution datasets as “out-of-the-box” entities to be used by the consuming teams. In particular, he talked about the misattribution potential in a complex microservice architecture where often intermediary results are cached. Sam described Ax and BoTorch?—?Ax