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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. This approach has a handful of benefits.

Traffic 339
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Event-Based Autoscaling: Ensuring Smooth Operations on Your Peak Days

DZone

In today’s world, companies often find themselves grappling with unpredictable surges in workloads, especially during pivotal events. This poses a significant challenge for businesses since miscalculations can lead to latency, lost customers, and significant financial losses, even as much as hundreds of thousands of dollars per minute.

Retail 169
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Rapid Event Notification System at Netflix

The Netflix TechBlog

To this end, we developed a Rapid Event Notification System (RENO) to support use cases that require server initiated communication with devices in a scalable and extensible manner. In this blog post, we will give an overview of the Rapid Event Notification System at Netflix and share some of the learnings we gained along the way.

Systems 334
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DevOps automation: From event-driven automation to answer-driven automation [with causal AI]

Dynatrace

They need event-driven automation that not only responds to events and triggers but also analyzes and interprets the context to deliver precise and proactive actions. These initial automation endeavors paved the way for greater advancements, leading to the next evolution of event-driven automation.

DevOps 228
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Seeing through hardware counters: a journey to threefold performance increase

The Netflix TechBlog

A quick canary test was free of errors and showed lower latency, which is expected given that our standard canary setup routes an equal amount of traffic to both the baseline running on 4xl and the canary on 12xl. What’s worse, average latency degraded by more than 50%, with both CPU and latency patterns becoming more “choppy.”

Hardware 363
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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim. To determine customer impact, we could compare various metrics such as error rates, latencies, and time to render.

Traffic 353
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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

In that scenario, the system would need to deal with the data propagation latency directly, for example, by use of timeouts or client-originated update tracking mechanisms. With traffic growth, a single leader node handling all request volume started becoming overloaded. Let’s assume a sequence of events E?…E??, of the data.

Cache 224