Using New Relic to Monitor Applications on OpenShift

Using New Relic to Monitor Applications on OpenShift

In this post, we show a way to configure New Relic to monitor an application running on OpenShift Container Platform. The repository includes a customized assemble script for Tomcat/JBoss EWS 8 that instruments an application using a New Relic Java Agent. The script for downloading and layering in New Relic Agent is baked into this assemble script.

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ASP.NET on OpenShift: Getting started in ASP.NET

ASP.NET on OpenShift: Getting started in ASP.NET

In parts 1 & 2 of this tutorial, I’ll be going over getting started quickly by using templates in Visual Studio Community 2015. This means that it’ll be for Windows in this part. However, I’ll go more in-depth with doing everything without templates in Visual Studio Code in a following tutorial, which will be applicable to Linux or Mac as well as Windows. If you’re not using Windows, you can still follow along in parts 1 & 2 to get a general idea of how to create a REST endpoint in .NET Core.

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OpenShift Commons Briefing #66: Microservices, .NET Core 1.1, and Kubernetes on OpenShift

OpenShift Commons Briefing #66: Microservices, .NET Core 1.1, and Kubernetes on OpenShift

In this briefing, Red Hat’s Todd Mancini, Senior Principal Product Manager and Don Schenck, Developer Advocate for .NET on Linux discussed some of the new application development highlights in Microsoft’s .NET Core 1.1:

• Over 1,300 new APIs since .NET Core 1.0.
• .NET Core 1.1 docker images from Red Hat’s container registry.
• Safe side-by-side installation with .NET Core 1.0.
• Performance improvements

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Red Hat Brings Cloud Native Services to Every Java Workload

Red Hat Brings Cloud Native Services to Every Java Workload

Red Hat is happy to announce the availability of a Java container image for cloud native workloads. Red Hat now expands the availability of cloud native packaging models to all Java applications that rely on OpenJDK and Maven. This builds on the proven S2I technology that has been available for OpenShift applications for many years.

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Fast Iterative Java Development on OpenShift/Kubernetes Using Rsync

Fast Iterative Java Development on OpenShift/Kubernetes Using Rsync

The key to a good development environment almost always comes down to how long it takes for changes you make to take effect. With any compiled language, there is often a lot of setup work involved to optimize deployment speed. Thankfully, one of the promises of containers is it allows for patterns to be standardized and repackaged as reusable images that do a lot of the heavy lifting for you.

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Performance Metrics (APM) for Spring Boot Microservices on OpenShift

Performance Metrics (APM) for Spring Boot Microservices on OpenShift

OpenShift provides a built-in monitoring tool called Hawkular. That tool is in charge of collecting metrics from Docker containers through the Kubernetes interface and storing, aggregating, and visualizing them. The metrics collected are CPU, Memory, Disk, and Network usage. Hawkular offers a “black-box” view of container performance but does not deal with application metrics like service performance or distribution of response time through application layers. For this specific case, the Hawkular community is working on another module called Hawkular APM that provides insight into the way an application executes across multiple (micro) services in a distributed (e.g. cloud) environment.

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Using Dynamic Provisioning and StorageClasses

Using Dynamic Provisioning and StorageClasses

OpenShift can integrate with underlying infrastructure, enabling OpenShift to dynamically interact with infrastructure and extend its functionality. Specifically, this can allow us to set up OpenShift to process a PersistentVolumeClaim and then allocate that storage dynamically.

I am going to cover what is needed to get started with dynamically provisioning storage, including cloud provider configuration, StorageClasses, and the Default StorageClass.

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Intro to Machine Learning using Tensorflow – Part 1

Intro to Machine Learning using Tensorflow - Part 1

Tensorflow is an open-source software library created by Google for Machine Intelligence. And Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text with others. Throughout this series, we’ll be using these two applications primarily, but we’ll also venture into other popular frameworks as well. By the end of this post, you’ll be able to run a linear regression (the “hello world” of ML) inside a container you built running in a cloud.

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