Red Hat OpenShift

Modern web applications on OpenShift: Part 3 — Openshift as a development environment

Modern web applications on OpenShift: Part 3 — Openshift as a development environment

Welcome back to the final part of this multipart series about deploying modern web applications on Red Hat OpenShift. In the first post, we took a look at how to deploy a modern web application using the fewest commands.

In the second part, we took a deeper look into how the new source-to-image (S2I) web app builder works and how to use it as part of a chained build.

This third and final part will take a look at how you can run your app’s “development workflow” on OpenShift.

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Leveraging OpenShift or Kubernetes for automated performance tests (part 3)

Leveraging OpenShift or Kubernetes for automated performance tests (part 3)

This is the third of a series of three articles based on a session I held at EMEA Red Hat Tech Exchange. In the first article, I presented the rationale and approach for leveraging Red Hat OpenShift or Kubernetes for automated performance testing, and I gave an overview of the setup. In the second article, we looked at building an observability stack. In this third part, we will see how the execution of the performance tests can be automated and related metrics gathered.

An example of what is described in this article is available in my GitHub repository.

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Using a local NuGet server with Red Hat OpenShift

Using a local NuGet server with Red Hat OpenShift

NuGet is the .NET package manager. By default, the .NET Core SDK will use packages from the nuget.org website.

In this article, you’ll learn how to deploy a NuGet server on Red Hat OpenShift Container Platform (RHOCP). We’ll use it as a caching server and see that it speeds up our builds. Before we get to that, we’ll explore some general NuGet concepts and see why it makes sense to use a local NuGet server.

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Leveraging OpenShift or Kubernetes for automated performance tests (part 2)

Leveraging OpenShift or Kubernetes for automated performance tests (part 2)

This is the second of a series of three articles based on a session I hold at EMEA Red Hat Tech Exchange. In the first article, I presented the rationale and approach for leveraging Red Hat OpenShift or Kubernetes for automated performance testing, and I gave an overview of the setup.

In this article, we will look at building an observability stack that, beyond the support it provides in production, can be leveraged during performance tests. This will provide insight into how the application performs under load.

An example of what is described in this article is available in my GitHub repository.

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Monitoring Node.js Applications on OpenShift with Prometheus

Monitoring Node.js Applications on OpenShift with Prometheus

Observability is Key

One of the great things about Node.js is how well it performs in a container. Its fast start up time, and relatively small size make it a favorite for microservice applications on OpenShift. But with this shift to containerized deployments comes some complexity. As a result, monitoring Node.js applications can be difficult. At times it seems as though the performance and behavior of our applications become opaque to us. So what can we do to find and address issues in our services before they become a problem? We need to enhance observability by monitoring the state of our services.

Instrumentation

Instrumentation of our applications is one way to increase observability. Therefore, in this article, I will demonstrate the instrumentation of a Node.js application using Prometheus.

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Integration of API management details (Part 4)

Integration of API management details (Part 4)

In Part 3 of this series, we started diving into the details that determine how your integration becomes the key to transforming your customer experience.

It started with laying out the process of how I’ve approached the use case by researching successful customer portfolio solutions as the basis for a generic architectural blueprint. Now it’s time to cover various blueprint details.

This article takes you deeper into specific elements (API management and reverse proxy) of the generic architectural overview.

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Building Java 11 and Gradle containers for OpenShift

Building Java 11 and Gradle containers for OpenShift

How do YOU get your Java apps running in a cloud?

First you grab a cloud from the sky by, for example,  (1) Getting started with a free account on Red Hat OpenShift Online, or (2) locally on your laptop using Red Hat Container Development Kit (CDK) or upstream Minishift on Windows, macOS, and Linux, or (3) using oc cluster up (only on Linux), or (4) by obtaining a login from someone running Red Hat OpenShift on a public or on-premises cloud. Then, you download the oc CLI client tool probably for Windows (and put it on your PATH). Then you select the Copy Login Command from the menu in the upper right corner under your name in the OpenShift Console’s UI, and you use, for example, the oc status command.

Great—now you just need to containerize your Java app. You could, of course, start to write your own Dockerfile, pick an appropriate container base image (and discuss Red Hat Enterprise Linux versus CentOS versus Fedora versus Ubuntu versus Debian versus Alpine with your co-workers; and, especially if you’re in an enterprise environment, figure out how to have that supported in production), figure out appropriate JVM startup parameters for a container, add monitoring, and so.

But perhaps what you really wanted to do today is…well, just get your Java app running in a cloud!

Read on to find an easier way.

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Building .NET Core container images using S2I

Building .NET Core container images using S2I

Red Hat OpenShift implements .NET Core support via a source-to-image (S2I) builder. In this article, we’ll take a closer look at how you can use that builder directly. Using S2I, you can build .NET Core application images without having to write custom build scripts or Dockerfiles. This can be useful on your development machine or as part of a CI/CD pipeline.

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Red Hat Container Development Kit 3.7 now available

Red Hat Container Development Kit 3.7 now available

We are pleased to announce the availability of the Red Hat Container Development Kit (CDK) 3.7. CDK 3.7 is based on Minishift v1.27.0, a command-line tool to quickly provision an OpenShift and Kubernetes cluster on your local machine for developing cloud- and container-based applications. The CDK also includes OpenShift Container Platform v3.11.14. You can use the CDK on Windows, macOS, or Linux.

Here’s a summary of the new features in CDK 3.7:

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Quickly try Red Hat Process Automation Manager in your cloud

Quickly try Red Hat Process Automation Manager in your cloud

It’s been some time since I last talked with you about putting JBoss BPM Suite (now called Red Hat Process Automation Manager) into your cloud, and with the new release, it’s time to talk AppDev in the cloud again.

It’s time to update the story and see how to put Red Hat Process Automation Manager in your cloud so you are set up with a standard configuration to start your first business rules project.

With the easy installation demo project described below, you can leverage process automation tooling through the business central web console running containerized on any Red Hat OpenShift.

Let’s take a closer look at how this works.

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