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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.

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Consistent caching mechanism in Titus Gateway

The Netflix TechBlog

The original assumptions and architectural choices were no longer viable. We introduce a caching mechanism in the API gateway layer, allowing us to offload processing from singleton leader elected controllers without giving up strict data consistency and guarantees clients observe. How do I know that my cache is up to date?

Cache 233
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PolyScale.ai – Scaling MySQL & PostgreSQL with Global Caching

Scalegrid

Data-driven applications span a wide breadth of complexity, from simple microservices to real-time event-driven systems under significant load. Modern application architectures such as the JAMstack enforce the separation […]. Guest post by Ben Hagan from PolyScale.ai

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models. To harness this data effectively, we employ a process of interaction tokenization, ensuring meaningful events are identified and redundancies are minimized.

Tuning 179
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AWS serverless services: Exploring your options

Dynatrace

To get a better understanding of AWS serverless, we’ll first explore the basics of serverless architectures, review AWS serverless offerings, and explore common use cases. Serverless architecture: A primer. Serverless architecture shifts application hosting functions away from local servers onto those managed by providers.

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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. This avoids thrashing caches too much for B and evens out the pressure on the L3 caches of the machine.

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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Moreover, common database optimizations like caching recently queried data don’t really work for alerting queries because, generally speaking, the last received datapoint is required for correctness. First and foremost, we have successfully alleviated our initial scalability problem with the polling based architecture. OK, Results?

Storage 297