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Developer Meet Designer
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Developer Meet Designer (Andres Galante & Brian Leathem)
Developer
Brian Leathem
@brianleathem
Vancouver
Designer
Andrés Galante
@andresgalante
Buenos Aires
Developer Meet Designer (Andres Galante & Brian Leathem)

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The fifth major release of Hibernate sports contains many internal changes developed in collaboration between the Hibernate team and the Red Hat middleware performance team. Efficient access to databases is crucial to get scalable and responsive applications. Hibernate 5 received much attention in this area. You’ll benefit from many of these improvements by merely upgrading. But it's important to understand some of these new, performance-boosting features because you will need to explicitly enable them. We'll explain the development background on all of these powerful new features and the investigation process for performance improvements. Our aim is to provide good guidance so you can make the most of it on your own applications. We'll also peek at other performance improvements made on JBoss EAP 7, like on the caching layer, the connection manager, and the web tier. We want to make sure you can all enjoy better-performing applications—that require less power and less servers—without compromising on your developer’s productivity.

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By Rafael Benevides and Christian Posta A lot of functionality necessary for running in a microservices architecture have been built into Kubernetes; why would you re-invent the wheel with lots of complicated client-side libraries? Have you ever asked why you should use containers and what are the benefits for your application? This talk will present a microservices application that have been built using different Java platforms: WildFly Swarm and Vert.x. Then we will deploy this application in a Kubernetes cluster to present the advantages of containers for MSA (Microservices Architectures) and DevOps. The attendees will learn how to create, edit, build, deploy Java Microservices, and also how to perform service discovery, rolling updates, persistent volumes and much more. Finally we will fix a bug and see how a CI/CD Pipeline automates the process and reduces the deployment time.

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This document discusses using metrics to monitor Quarkus applications. It recommends metrics like throughput, memory usage, queue time, average response time, and error rates. It explains how Quarkus supports Micrometer for instrumenting applications with metrics and integrating with monitoring systems. The document includes a demo of adding metrics to code. It provides tips for using annotations and tags to gain more insights from metrics. Source code examples are linked.

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