Remove Efficiency Remove Latency Remove Tuning
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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.

Tuning 165
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How to Optimize CPU Performance Through Isolation and System Tuning

DZone

CPU isolation and efficient system management are critical for any application which requires low-latency and high-performance computing. To achieve this level of performance, such systems require dedicated CPU cores that are free from interruptions by other processes, together with wider system tuning.

Tuning 253
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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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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 166
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Kafka scales efficiently for large data workloads, while RabbitMQ provides strong message durability and precise control over message delivery. Message brokers handle validation, routing, storage, and delivery, ensuring efficient and reliable communication. This allows Kafka clusters to handle high-throughput workloads efficiently.

Latency 147
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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. In a serverless architecture, applications are distributed to meet demand and scale requirements efficiently.

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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? Stay tuned for a closer look at the innovation behind thescenes! The stakes are even higher when ensuring every title launches flawlessly.

Traffic 172