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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. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

Tuning 210
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How To Scale a Single-Host PostgreSQL Database With Citus

Percona

Rather than listing the concepts, function calls, etc, available in Citus, which frankly is a bit boring, I’m going to explore scaling out a database system starting with a single host. And now, execute the benchmark: -- execute the following on the coordinator node pgbench -c 20 -j 3 -T 60 -P 3 pgbench The results are not pretty.

Database 102
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What Adrian Did Next: 2022 Conference Appearances

Adrian Cockcroft

photo by Adrian I gave a talk at Monitorama in Portland Oregon in June, which set out the idea that carbon is just another metric to monitor, and that in a few years most of the monitoring and performance tuning tools are going to be reporting and optimizing for carbon alongside latency, throughput, availability and cost.

AWS 52
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Towards a Reliable Device Management Platform

The Netflix TechBlog

System Setup Architecture The following diagram summarizes the architecture description: Figure 1: Event-sourcing architecture of the Device Management Platform. A timeline of the transition from Spring KafkaListener to Alpakka-Kafka is presented here for a better understanding of the motivations for the transition.

Latency 213
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QCon London: Lessons Learned From Building LinkedIn’s AI/ML Data Platform

InfoQ

The presenter shared the lessons learned from evolving and operating the platform, including cluster management and library versioning. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz

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MongoDB Best Practices: Security, Data Modeling, & Schema Design

Percona

The main objective of this post is to share my experience over the past years tuning MongoDB and centralize the diverse sources that I crossed in this journey in a unique place. The swap issue is explained in the excellent article by Jeremy Cole at the Swap Insanity and NUMA Architecture. Two other schedulers are deadline and noop.

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A case for managed and model-less inference serving

The Morning Paper

HotOS’19 is presenting me with something of a problem as there are so many interesting looking papers in the proceedings this year it’s going to be hard to cover them all! The following figure highlights how just one of these variables, batch size, impacts throughput and latency on ResNet50. HotOS’19.