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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

Latency 212
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Edge Data Platforms, Real-Time Services, and Modern Data Trends

DZone

We all know that data is being generated at an unprecedented rate. You may also know that this has led to an increase in the demand for efficient and secure data storage solutions that won’t break the bank. This article will explore what edge data platforms and real-time services are, why they are important, and how they can be used.

IoT 130
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Data Reprocessing Pipeline in Asset Management Platform @Netflix

The Netflix TechBlog

This platform has evolved from supporting studio applications to data science applications, machine-learning applications to discover the assets metadata, and build various data facts. Hence we built the data pipeline that can be used to extract the existing assets metadata and process it specifically to each new use case.

Media 237
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MongoDB Rollback: How to Minimize Data Loss

Scalegrid

When a MongoDB rollback happens, it can cause trouble to your data integrity and system consistency. Understanding how to address a rollback is critical for minimizing potential data loss and maintaining seamless operations. With direct, actionable insights, prepare to navigate the complexities of rollbacks with confidence.

Database 130
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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

Reduced tail latencies In both our GRPC and DGS Framework services, GC pauses are a significant source of tail latencies. For a given CPU utilization target, ZGC improves both average and P99 latencies with equal or better CPU utilization when compared to G1.

Latency 228
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. In this way, no human intervention is required in the remediation process. Multi-objective optimizations.

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Transforming Business Outcomes Through Strategic NoSQL Database Selection

DZone

Factors like read and write speed, latency, and data distribution methods are essential. For instance, rapid read and write operations are crucial for applications requiring real-time data analytics. Yet, they are often evaluated in isolation, removed from the business context.

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