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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. Logging is selective to cases where the old and new responses do not match.

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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

Central to this infrastructure is our use of multiple online distributed databases such as Apache Cassandra , a NoSQL database known for its high availability and scalability. It also serves as central configuration of access patterns such as consistency or latency targets. Useful for keeping “n-newest” or prefix path deletion.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Such frameworks support software engineers in building highly scalable and efficient applications that process continuous data streams of massive volume. Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively.

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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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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. divide the input video into small chunks 2.

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The Netflix Cosmos Platform

The Netflix TechBlog

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. The third generation, called Reloaded , has been online for about seven years and has proven to be stable and massively scalable.

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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

The data warehouse is not designed to serve point requests from microservices with low latency. Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store. Figure 1 shows how we use Bulldozer to move data at Netflix. Moving data with Bulldozer at Netflix.

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