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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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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. Similarly, an increased throughput signifies an intensive workload on a server and a larger latency.

Metrics 130
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Mastering MongoDB® Timeout Settings

Scalegrid

For example, if there’s some trouble connecting due to networking problems or excessive load requests from customers present on the server’s side, that might result in a resolution-related delay causing a subsequent timeout problem. Fine-tuning these settings would lead to improved performance and provide an enhanced user experience. <p>The

Java 130
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Build and operate multicloud FaaS with enhanced, intelligent end-to-end observability

Dynatrace

Higher latency and cold start issues due to the initialization time of the functions. Data visualization : how to present, explore and interpret observability data from serverless functions intuitively, clearly, and holistically? Enable faster development and deployment cycles by abstracting away the infrastructure complexity.

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Best Practices for a Seamless MongoDB Upgrade

Percona

Improved performance : MongoDB continually fine-tunes its database engine, resulting in faster query execution and reduced latency. Navigating common MongoDB upgrade challenges Even with a well-thought-out plan, MongoDB upgrades can present challenges.

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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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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