Operating System

Rootless containers with Podman: The basics

Rootless containers with Podman: The basics

As a developer, you have probably heard a lot about containers. A container is a unit of software that provides a packaging mechanism that abstracts the code and all of its dependencies to make application builds fast and reliable. An easy way to experiment with containers is with the Pod Manager tool (Podman), which is a daemonless, open source, Linux-native tool that provides a command-line interface (CLI) similar to the docker container engine.

In this article, I will explain the benefits of using containers and Podman, introduce rootless containers and why they are important, and then show you how to use rootless containers with Podman with an example. Before we dive into the implementation, let’s review the basics.

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New C++ features in GCC 10

New C++ features in GCC 10

The GNU Compiler Collection (GCC) 10.1 was released in May 2020. Like every other GCC release, this version brought many additions, improvements, bug fixes, and new features. Fedora 32 already ships GCC 10 as the system compiler, but it’s also possible to try GCC 10 on other platforms (see godbolt.org, for example). Red Hat Enterprise Linux (RHEL) users will get GCC 10 in the Red Hat Developer Toolset (RHEL 7), or the Red Hat GCC Toolset (RHEL 8).

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How to switch Red Hat OpenShift Virtualization from hardware virtualization to software emulation

How to switch Red Hat OpenShift Virtualization from hardware virtualization to software emulation

OpenShift Virtualization is a feature of Red Hat OpenShift Container Platform (OCP) and OpenShift Kubernetes Engine that allows you to run and manage virtual machine workloads alongside container workloads. Based on the open source project KubeVirt, the goal of OpenShift Virtualization is to help enterprises move from a VM-based infrastructure to a Kubernetes-and-container-based stack, one application at a time.

In my previous article, I showed you how to set up and enable OpenShift Virtualization running on Amazon Web Services Elastic Compute Cloud (AWS EC2). In that article, I noted that OpenShift Virtualization looks for hardware virtualization by default, which requires a bare-metal server instance. If you are running OpenShift on AWS EC2, as I do, then you have to enable software emulation over the default hardware virtualization. Otherwise, you need a bare-metal instance from the public cloud provider or a pure bare-metal solution.

In this article, I show you how to switch OpenShift Virtualization from its default of hardware virtualization to QEMU-based software emulation. You will then be able to start and operate a virtual machine through OpenShift Virtualization, even in a non-bare metal instance such as AWS EC2.

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Using OpenAPI with .NET Core

Using OpenAPI with .NET Core

In this article, we’ll look at using OpenAPI with .NET Core. OpenAPI is a specification for describing RESTful APIs. First, I’ll show you how to use OpenAPI to describe the APIs provided by an ASP.NET Core service. Then, we’ll use the API description to generate a strongly-typed client to use the web service with C#.

Writing OpenAPI descriptions

Developers use the OpenAPI specification to describe RESTful APIs. We can then use OpenAPI descriptions to generate a strongly-typed client library that is capable of accessing the APIs.

Note: Swagger is sometimes used synonymously with OpenAPI. It refers to a widely used toolset for working with the OpenAPI specification.

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How to run Red Hat CodeReady Containers on Windows 10 Enterprise

How to run Red Hat CodeReady Containers on Windows 10 Enterprise

Red Hat CodeReady Containers allows you to spin up a small Red Hat OpenShift cluster on your local PC, with the need for a server, a cloud, or a team of operations people. For developers who want to get started immediately with cloud-native development, containers, and Kubernetes (as well as OpenShift), it’s a simple and slick tool. It runs on macOS, Linux, and all versions of Windows 10.

Except for Windows 10 Enterprise.

Which I painfully learned.

Because I lazily didn’t pay attention to the documentation.

OK, so I’m the only developer who glosses over documentation. Fortunately for you, I struggled and managed to get CRC running on my Windows 10 Enterprise notebook computer, and this article explains what is involved to get it working. So, in a sense, you’re welcome that I’m lazy.

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Install Red Hat OpenShift Operators on your laptop using Red Hat CodeReady Containers and Red Hat Marketplace

Install Red Hat OpenShift Operators on your laptop using Red Hat CodeReady Containers and Red Hat Marketplace

Red Hat CodeReady Containers (CRC) is the quickest way for developers to get started with clusters on Red Hat OpenShift 4.1 or newer. CodeReady Containers is designed to run on a local computer. It simplifies setup and testing by emulating the cloud development environment locally with all of the tools that you need to develop container-based applications.

Red Hat Marketplace is an open cloud marketplace that makes it easy to discover and purchase the certified, containerized tools you need to build enterprise-first applications. It was created to help developers using OpenShift build applications and deploy them across a hybrid cloud. Red Hat Marketplace works on any developer workstation that is running CodeReady Containers.

This article guides you through the steps of setting up Red Hat Marketplace and installing containerized products in your local CodeReady Containers-based OpenShift clusters.

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Migrate your Java apps to containers with Migration Toolkit for Applications 5.0

Migrate your Java apps to containers with Migration Toolkit for Applications 5.0

As a developer, you have probably experimented with Kubernetes. It’s also possible that you are already running several Java applications on a Kubernetes platform, maybe Red Hat OpenShift. These initial containerized applications were greenfield projects, where you enjoyed the benefits of a platform providing templated deployments, easy rollbacks, resource availability, security by default, and a manageable way to publish your services.

Now, you might be thinking, “How can I enjoy all of these benefits in my existing Java applications?” Most Java applications in production today are running on virtual machines (VMs), likely on an application platform that is not container friendly. So, how can you migrate them from the current platform to containers on Kubernetes?

It isn’t an easy task, but this is a problem that we have been working hard on for years. Red Hat’s Migration Toolkit for Applications (MTA) 5.0 is the latest resulting iteration: An assembly of tools that you can use to analyze existing applications and discover what is required to modernize them. Read on to learn MTA 5.0’s features and migration paths.

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Using Spring Cloud Functions with OpenShift Serverless

Using Spring Cloud Functions with OpenShift Serverless

Spring Cloud Functions are yet another interesting option for Java developers when building serverless applications. You have already seen how to build and run applications for Red Hat OpenShift Serverless using Quarkus, but in this article, we talk about how to use Spring Cloud Functions and walk you through those steps. These steps are similar to running any other Spring Boot application with OpenShift Serverless. One of the benefits of building an open hybrid serverless platform is giving developers a choice of programming languages, tools, frameworks, and portability across any environment to run serverless applications. Beyond that, you want to ensure that the developer experience and overall workflow is intuitive and practical, which is what you will learn here.

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Enable OpenShift Virtualization on Red Hat OpenShift

Enable OpenShift Virtualization on Red Hat OpenShift

Imagine an information technology (IT) world where everything is ideal: Every company has switched over to cloud-native applications, every application is containerized, everything is automated, and the IT people see that the world is good. Things are not so ideal in the real world, though, as we know. Applications remain tightly coupled with traditional virtual machine (VM) resources such as software libraries and hardware resources. The effort to migrate them from VMs to containers seems insurmountable, requiring years of dedicated spending and hours from developers and software architects.

The dilemma is that companies want all of their applications to eventually run on containers, but they also need to support applications running on VMs until that glorious shift happens. Given that application migration from VMs to containers will happen over the long haul, some companies are exploring a lift-and-shift approach. In theory, lift-and-shift would let us migrate tightly-coupled legacy applications to a container platform like Red Hat OpenShift. Rather than rewriting application code, developers would simply write interfaces (essentially, code with patterns) that are compatible with the existing structure.

Unfortunately, this scenario is unrealistic for legacy projects involving hundreds of application modules and packages. Therefore, it is logical to ask: What if there was a way to support existing applications running on virtual machines and new applications running on containers in one unified container-based platform?

Luckily, there is a way: Use a Kubernetes-based platform like OpenShift.

In this article, I introduce OpenShift Virtualization, a feature for Red Hat OpenShift Container Platform (OCP). OpenShift Virtualization allows you to run and manage virtual-machine workloads alongside container workloads.

Note: As of version 2.4 when CNV went GA, Container-Native Virtualization was renamed OpenShift Virtualization.

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Developing micro-microservices in C on Red Hat OpenShift

Developing micro-microservices in C on Red Hat OpenShift

Java holds its dominating position in enterprise middleware for good reasons; however, describing anything in Java as “micro” requires a generous interpretation. It isn’t unusual to find Java-based microservices that need half a gigabyte of RAM to provide modest functionality at a modest load. The trend toward serverless architectures, where services are started and stopped according to demand, does little to improve the situation.

It has recently become possible to compile Java to a native executable using tools like GraalVM. This technique, coupled with an optimized Java runtime like Quarkus, tames Java’s resource consumption to some extent.

Nevertheless, we should not lose sight of programming languages that were designed from the start to compile to native code, with little to no runtime overhead. Languages like Rust and Go have become popular, and justifiably so. For optimal runtime resource usage and millisecond startup times, though, it remains hard to beat C.

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