Watch this video for an introduction to CodeReady Workspaces and Red Hat OpenShift Application Runtimes, their functionality, and how they complement each other for cloud-native application development on OpenShift.
Continue reading “Getting started with CodeReady Workspaces and Red Hat OpenShift Application Runtimes”
The concept of RPM packaging can be overwhelming for first-timers because of the impression a steep learning curve is involved. In this article, I will demonstrate that building an RPM with minimal knowledge and experience is possible. Note that this article is meant as a starting point, not a complete guide to RPM packaging.
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JDK Mission Control is now the newest member of the Red Hat Software Collections (RHSCL). JDK Mission Control is a powerful profiler for HotSpot Java virtual machines (JVMs) and has an advanced set of tools that enable efficient and detailed analysis of the extensive data collected by JDK Flight Recorder. The toolchain enables developers and administrators to collect and analyze data from Java applications running locally or deployed in production environments.
In this article, I will go through a primary example of setting up JDK Mission Control. For Linux, JDK Mission Control is part of the RHSCL and, for Windows, it is available as part of the OpenJDK zip distribution on the Red Hat Customer Portal. For Linux, these instructions assume that Red Hat Build of OpenJDK 11 is already installed. I will show how to set up the system to install software from RHSCL, which provides the latest development technologies for Red Hat Enterprise Linux. Then, I will install the JDK Mission Control and run a simple sample application. The whole tutorial should take fewer than 10 minutes to complete.
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“The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” (Edsger W. Dijkstra)
Rule-based artificial intelligence (AI) is often overlooked, possibly because people think it’s only useful in heavyweight enterprise software products. However, that’s not necessarily true. Simply put, a rule engine is just a piece of software that allows you to separate domain and business-specific constraint from the main application flow. We are part of the team developing and maintaining Drools—the world’s most popular open source rule engine and part of Red Hat—and, in this article, we will describe how we are changing Drools to make it part of the cloud and serverless revolution.
Continue reading “Quarking Drools: How we turned a 13-year-old Java project into a first-class serverless component”
Have you tried the Red Hat Enterprise Linux 8 (RHEL8) Beta yet? Read on to learn how to stand up a LAMP stack on top of RHEL8 Beta quickly, and play around with new features built into the operating system.
A LAMP stack is made up out of four main components, and some glue. The first main component in a LAMP stack is Linux. In my example, I’m using Red Hat Enterprise Linux 8 Beta for that, which gives me a secure operating system, a modern programming environment, and user-friendly set of tools to control it.
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As part of the GCC developers‘ on-demand range work for GCC 10, I’ve been playing with improving the backward jump threader so it can thread paths that are range-dependent. This, in turn, had me looking at the jump threader, which is a part of the compiler I’ve been carefully avoiding for years. If, like me, you’re curious about compiler optimizations, but are jump-threading-agnostic, perhaps you’ll be interested in this short introduction.
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In part 1, I shed light on trade-offs involved in the GCC implementation choices for various types of front-end warnings, such as preprocessor warnings, lexical warnings, type-safety warnings, and other warnings.
As useful as front-end warnings are, those based on the flow of control or data through the program have rather inconvenient limitations. To overcome them, flow-based warnings have increasingly been implemented in what GCC calls the “middle end.” Middle-end warnings are the focus of this article.
Continue reading “Understanding GCC warnings, Part 2”
Most of us appreciate when our compiler lets us know we made a mistake. Finding coding errors early lets us correct them before they embarrass us in a code review or, worse, turn into bugs that impact our customers. Besides the compulsory errors, many projects enable additional diagnostics by using the -Wall and -Wextra command-line options. For this reason, some projects even turn them into errors via -Werror as their first line of defense. But not every instance of a warning necessarily means the code is buggy. Conversely, the absence of warnings for a piece of code is no guarantee that there are no bugs lurking in it.
In this article, I would like to shed more light on trade-offs involved in the GCC implementation choices. Besides illuminating underlying issues for GCC contributors interested in implementing new warnings or improving existing ones, I hope it will help calibrate expectations for GCC users about what kinds of problems can be expected to be detected and with what efficacy. Having a better understanding of the challenges should also reduce the frustration the limitations of the available solutions can sometimes cause. (See part 2 to learn more about middle-end warnings.)
The article isn’t specific to any GCC version, but some command-line options it refers to are more recent than others. Most are in GCC 4 that ships with Red Hat Enterprise Linux (RHEL), but some are as recent as GCC 7. The output of the compiler shown in the examples may vary between GCC versions. See How to install GCC 8 on RHEL if you’d like to use the latest GCC.
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The rise of microservices architectures drastically changed the software development landscape. In the past few years, we have seen a shift from centralized monoliths to distributed computing that benefits from cloud infrastructure. With distributed deployments, the adoption of microservices, and system scaling to cloud levels, new problems emerged, as well as new components that tried to solve the problems.
By now, you most likely have heard that the service mesh or Istio is here to save the day. However, you might be wondering how it fits with your current enterprise integration investments and API management initiatives. That is what I discuss in this article.
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This article contains a workaround for Red Hat Container Development Kit (CDK) suddenly failing to start. If you are getting the message “Checking if requested OpenShift version ‘v3.11.82’ is valid … FAIL,” see the solution below.
Continue reading CDK workaround for failing OpenShift version check
