I’ve had many proud moments in my role here at Red Hat over the years. Examples include when we released the first version of WildFly, when we acquired the Camel team, when we worked with other vendors to create Eclipse MicroProfile, the great work the Strimzi team did to get into the Cloud Native Computing Foundation, our entire Red Hat Managed Integration effort, Kogito, and the list goes on. I feel like I add to this list of examples on an almost weekly basis.
Well, I can now update this list with the first product release of Quarkus, formally called the Red Hat build of Quarkus. (You can also find more support options on the Quarkus project site.) It should come as no surprise that Quarkus is on this list. I suppose what might surprise some people is that Quarkus is only just a product now. Given all of the activities since we officially launched the Quarkus project in 2019, you could be forgiven for thinking it was already a product.
Continue reading “The road to Quarkus GA: Completing the first supported Kubernetes-native Java stack”
In many organizations, it is a struggle for developers to get custom Jenkins container images created. Fortunately, in engineering, there is often more than one way to get the job done. In this article, I show you how to create your own custom Jenkins container image by aggregating readily available containers in a pod template.
Continue reading An easier way to create custom Jenkins containers
In the previous article, I discussed the benefits of C and C++ language restrictions in optimized code. In this second half, I present a variety of programming language exemptions and compiler extensions that developers can use to get around aliasing restrictions more or less safely. I will also discuss the common pitfalls of aliasing, both resulting from the extensions as well as from misuses of standard language constructs, and illustrate common problems these pitfalls might cause.
Continue reading “The joys and perils of aliasing in C and C++, Part 2”
It is just a few short weeks since we released Open Data Hub (ODH) 0.6.0, bringing many changes to the underlying architecture and some new features. We found a few issues in this new version with the Kubeflow Operator and a few regressions that came in with the new JupyterHub updates. To make sure your experience with ODH 0.6 does not suffer because we wanted to release early, we offer a new (mostly) bugfix release: Open Data Hub 0.6.1.
Continue reading Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions
In C, C++, and some other programming languages, the term aliasing refers to a situation where two different expressions or symbols refer to the same object. When references access that object in different ways—as both reads and stores—there are consequences for the order in which these mixed accesses can happen. The value that is stored first is expected to be read by the subsequent access. In many instances, aliasing is harmless: It is common, safe, and usually optimally efficient to use two pointers of the same type to read, and even to write to the same object. But in some cases, using aliasing symbols for mixed accesses is less benign, and can adversely affect the correctness or efficiency of your code.
Although there are quite a few articles on this subject, most tend to focus on the rules and requirements outlined in the standards (such as the strict aliasing rule). In this article, I focus on the details of the C and C++ language restrictions, their challenges and pitfalls, and examples demonstrating the restrictions’ beneficial effects in optimized code. In Part 2, I will present exemptions from aliasing, which can help you get around the restrictions more or less safely. I also consider some of the common pitfalls of aliasing and mixed accesses, and the actual problems these pitfalls might cause.
Continue reading “The joys and perils of C and C++ aliasing, Part 1”
The fabric8 Maven plug-in, often abbreviated FMP, can be added to a Maven Java project and takes care of the administrative tasks involved in deploying the application to a Red Hat OpenShift cluster. These tasks include:
- Creating an OpenShift build configuration (BC).
- Coordinating the source-to-image (S2I) process to create a container image from the application’s compiled bytecode.
- Creating and instantiating a deployment configuration (DC) from information in the project.
- Defining and instantiating OpenShift services and routes.
All of the relevant components of this process are well-documented individually. This article pulls together documentation sources to create an overview of how the plug-in works, and the structure of the image it generates—which might make the plug-in easier to use and troubleshoot.
Continue reading “How the fabric8 Maven plug-in deploys Java applications to OpenShift”
Red Hat Software Collections 3.5 and Red Hat Developer Toolset 9.1 are now available for Red Hat Enterprise Linux 7. Here’s what that means for developers.
Red Hat Software Collections (RHSCL) is how we distribute the latest stable versions of various runtimes and languages through Red Hat Enterprise Linux (RHEL) 7, with some components available in RHEL 6. RHSCL also contains the Red Hat Developer Toolset, which is the set of tools we curate for C/C++ and Fortran. These components are supported for up to five years, which helps you build apps that have a long lifecycle as well.
Continue reading “Red Hat Software Collections 3.5 brings updates for Red Hat Enterprise Linux 7”
It isn’t always easy to understand how Open vSwitch (OVS) cycles are spent, especially because various parameters and configuration options can affect how OVS behaves. Members of the Open vSwitch community are actively working to understand what causes packets drops in Open vSwitch. Efforts so far have included adding a custom statistic for vHost TX retries, tracking vHost TX contention, and adding a coverage counter to count vHost IRQs. We are particularly interested in the user space datapath that uses the Data Plane Development Kit (DPDK) for fast I/O.
Adding these statistics is an ongoing effort, and we won’t cover all of the corners. In some cases, the statistics leave doubts about what is causing a behavior.
In this article, I will introduce a new counter we’ve added to learn more about contention in the vHost transmission path. I’ll also show you how to use the new counter with
perf, and I’ll discuss what’s next for our ongoing efforts.
Continue reading “Debugging vHost user TX contention in Open vSwitch”
Java was introduced 25 years ago, and to this day, remains one of the most popular programming languages among developers. However, Java has developed a reputation for not being a good fit for cloud-native applications. Developers look for (and often choose) alternative frameworks such as Go and Node.js to support their cloud-native development requirements.
Why learn another language when you can use your existing skills? Quarkus allows Java developers to leverage their expertise to develop cloud-native, event-driven, reactive, and serverless applications. Quarkus provides a cohesive Java platform that feels familiar but new at the same time. Not only does it leverage existing Java standards, but it also provides a number of features that optimize developer joy, including live coding, unified configuration, IDE plugins, and more.
Recently, Red Hat announced support for Quarkus. With Quarkus, Red Hat advances Java on Kubernetes and bridges the gap between traditional Java applications and cloud-native environments.
Continue reading “Quarkus, a Kubernetes-native Java runtime, now fully supported by Red Hat”
Go is an increasingly popular programming language, and frequently chosen for developing command-line utilities. Many tools used with Kubernetes and Red Hat OpenShift are written in Go, including the command-line interfaces (CLIs) for Tekton (
tkn), OpenShift (
oc), and Kubernetes (
kubectl). Also, developers can compile Go to a single executable for a broad range of operating systems. As a result, it’s easy to develop and desk-test applications before putting them into containers and running those containers in OpenShift.
In a meta sort of way, this is an article about a tutorial, where I show you how to build and deliver a small Go RESTful service using OpenShift Pipelines. You could just jump to the tutorial now, but I suggest reading this article first. I’ll quickly introduce the working environment for the tutorial, and I’ll explain my logic for setting up the tutorial the way that I did.
Continue reading “Build a Go application using OpenShift Pipelines”