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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs. It facilitates the distribution of these learnings to other models, either through shared model weights for fine tuning or directly through embeddings. However, certain features require special attention.

Tuning 165
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Its partitioned log architecture supports both queuing and publish-subscribe models, allowing it to handle large-scale event processing with minimal latency. Apache Kafka uses a custom TCP/IP protocol for high throughput and low latency. Apache Kafka, designed for distributed event streaming, maintains low latency at scale.

Latency 147
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Introducing Impressions at Netflix

The Netflix TechBlog

Analyzing impression history, for example, might help determine how well a specific row on the home page is functioning or assess the effectiveness of a merchandising strategy. Automating Performance Tuning with Autoscalers Tuning the performance of our Apache Flink jobs is currently a manual process.

Tuning 166
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? Some examples: Why is title X not showing on the Coming Soon row for a particular member?

Traffic 172
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Performance Tuning Java Applications in Linux

DZone

You may also like: How to Properly Plan JVM Performance Tuning. While Performance Tuning an application both Code and Hardware running the code should be accounted for. Polling threads are an example where you might want to do this. For low latency, applications use Concurrent Mark and Sweep Algorithm — CMS or G1 GC.

Java 147
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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. For example, if some fields in the responses are timestamps, those will differ.

Traffic 347