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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience.

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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. Active data includes jobs and tasks that are currently running. Titus Gateway handles user requests.

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The Power of Caching: Boosting API Performance and Scalability

DZone

Caching is the process of storing frequently accessed data or resources in a temporary storage location, such as memory or disk, to improve retrieval speed and reduce the need for repetitive processing.

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Second, developers had to constantly re-learn new data modeling practices and common yet critical data access patterns. To overcome these challenges, we developed a holistic approach that builds upon our Data Gateway Platform. Data Model At its core, the KV abstraction is built around a two-level map architecture.

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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. In such cases, we were not testing for response data but overall behavior.

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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction 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.

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DBLog: A Generic Change-Data-Capture Framework

The Netflix TechBlog

Andreas Andreakis , Ioannis Papapanagiotou Overview Change-Data-Capture (CDC) allows capturing committed changes from a database in real-time and propagating those changes to downstream consumers [1][2]. No locks on tables are ever acquired, which prevent impacting write traffic on the source database. Writing events to any output.

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