Remove Latency Remove Strategy 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 212
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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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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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Introducing Impressions at Netflix

The Netflix TechBlog

We can experiment with different content placements or promotional strategies to boost visibility and engagement. 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.

Tuning 166
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

With its exchange feature, RabbitMQ enables advanced routing strategies, making it well-suited for workflows that require controlled message flow and guaranteed delivery. Its partitioned log architecture supports both queuing and publish-subscribe models, allowing it to handle large-scale event processing with minimal latency.

Latency 147
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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!

Traffic 172
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively. After failures, Kafka Streams’ partition assignment strategy, triggered by rebalances, causes its executions to accumulate more lag. This significantly increases event latency.