Using Red Hat Application Migration Toolkit to see the impact of migrating to OpenJDK

Using Red Hat Application Migration Toolkit to see the impact of migrating to OpenJDK

Migrating from one software solution to another is a reality that all good software developers need to plan for. Having a plan helps to drive innovation at a continuous pace, whether you are developing software for in-house use or you are acquiring software from a vendor. In either case, never anticipating or planning for migration endangers the entire innovation value proposition. And in today’s ever-changing world of software, everyone who wants to benefit from the success of the cloud has to ensure that cloud innovation is continuous. Therefore, maintaining a stack that is changing along with technological advancements is a necessity.

In this article, we will take a look at the impact of moving to OpenJDK and the results will aid in drawing further conclusions and in planning. It’s quite common to be using a proprietary version of JDK, and this article addresses how to use Red Hat Application Migration Toolkit to analyze your codebase to understand the impact of migrating to OpenJDK.

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Speeding up Open vSwitch with partial hardware offloading

Speeding up Open vSwitch with partial hardware offloading

Open vSwitch (OVS) can use the kernel datapath or the userspace datapath. There are interesting developments in the kernel datapath using hardware offloading through the TC Flower packet classifier, but in this article, the focus will be on the userspace datapath accelerated with the Data Plane Development Kit (DPDK) and its new feature—partial flow hardware offloading—to accelerate the virtual switch even more.

This article explains how the virtual switch worked before versus now and why the new feature can potentially save resources while improving the packet processing rate.

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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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Using the Yeoman Camel-Project generator to jump start a project

Using the Yeoman Camel-Project generator to jump start a project

The Red Hat Fuse Tooling team recently broadened its focus from a cross-platform, single-IDE (Eclipse) approach to a cross-platform, cross-IDE approach (Eclipse, VS Code, Che), starting several concerted efforts to provide tools that work across platforms and development environments. Supporting VS Code has become a priority that led us to explore using the Yeoman framework for project and file generation to provide developers a way to jump start their Fuse/Camel development efforts.

This article describes the Yeoman framework and the new Yeoman-based Camel-Project generator the Fuse Tooling team created, and it shows how to install and run the generator.

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Integration of container platform essentials (Part 5)

Integration of container platform essentials (Part 5)

In Part 4 of this series, we looked into details that determine how your integration becomes the key to transforming your omnichannel 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 more blueprint details.

This article discusses the core elements in the blueprint (container platform and microservices) that are crucial to the generic architectural overview.

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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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Performance improvements in OVN: Past and future

Performance improvements in OVN: Past and future

OVN (Open Virtual Network) is a subcomponent of Open vSwitch (OVS). It allows for the expression of overlay networks by connecting logical routers and logical switches. Cloud providers and cloud management systems have been using OVS for many years as a performant method for creating and managing overlay networks.

Lately, OVN has come into its own because it is being used more in Red Hat products. The result has been an increased amount of scrutiny for real-world scenarios with OVN. This has resulted in new features being added to OVN. More importantly, this has led to tremendous changes to improve performance in OVN.

In this article, I will discuss two game-changing performance improvements that have been added to OVN in the past year, and I will discuss future changes that we may see in the coming year.

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Using a Kotlin-based gRPC API with Envoy proxy for server-side load balancing

Using a Kotlin-based gRPC API with Envoy proxy for server-side load balancing

These days, microservices-based architectures are being implemented almost everywhere. One business function could be using a few microservices that generate lots of network traffic in the form of messages being passed around. If we can make the way we pass messages more efficient by having a smaller message size, we could  the same infrastructure to handle higher loads.

Protobuf (short for “protocol buffers”) provides language- and platform-neutral mechanisms for serializing structured data for use in communications protocols, data storage, and more. gRPC is a modern, open source remote procedure call (RPC) framework that can run anywhere. Together, they provide an efficient message format that is automatically compressed and provides first-class support for complex data structures among other benefits (unlike JSON).

Microservices environments require lots of communication between services, and for this to happen, services need to agree on a few things. They need to agree on an API for exchanging data, for example, POST (or PUT) and GET to send and receive messages. And they need to agree on the format of the data (JSON). Clients calling the service also need to write lots of boilerplate code to make the remote calls (frameworks!). Protobuf and gRPC provide a way to define the schema of the message (JSON cannot) and generate skeleton code to consume a gRPC service (no frameworks required).

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Eclipse Che 7 is Coming and It’s Really Hot (4/4)

Eclipse Che 7 is Coming and It’s Really Hot (4/4)

Eclipse Che 7 is an enterprise-grade IDE that is designed to solve many of the challenges faced by enterprise development teams. In my previous articles, I covered the main focus areas for Eclipse Che 7, the new plugin model, and kube-native developer workspaces. This article explains security and management of Eclipse Che 7 in enterprise deployment scenarios as well as release timing.

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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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