Red Hat Fuse is a leading integration platform, which is capable of solving any given problem with simple enterprise integration patterns (EIP). Over time, Red Hat Fuse has evolved to cater to a wide range of infrastructure needs.
For more information on each of these, check out the Red Hat Fuse documentation. The Fuse on Red Hat OpenShift flavor uses a Fuse image that has runtime components packaged inside a Linux container image. This article will discuss how to reduce the size of the Fuse image. The same principle can be used for other images.
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Back in May, we launched the Red Hat Universal Base Image (UBI), targeted at developers building containerized applications for the cloud. Since then, we have published an extensive FAQ covering topics ranging from how often UBI is updated, to how the end user license agreement (EULA) allows you to redistribute applications built on it. These are all great fundamental topics to cover, but people still seem to have a lot of questions around what UBI is and what it isn’t.
Continue reading What is Red Hat Universal Base Image?
Python has become a popular programming language in the AI/ML world. Projects like TensorFlow and PyTorch have Python bindings as the primary interface used by data scientists to write machine learning code. However, distributing AI/ML-related Python packages and ensuring application binary interface (ABI) compatibility between various Python packages and system libraries presents a unique set of challenges.
The manylinux standard (e.g., manylinux2014) for Python wheels provides a practical solution to these challenges, but it also introduces new challenges that the Python community and developers need to consider. Before we delve into these additional challenges, we’ll briefly look at the Python ecosystem for packaging and distribution.
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This article demonstrates an application update scenario which leverages Red Hat OpenShift image streams together with standard Kubernetes native resources. It also shows how image streams automatically redeploy application pods after an update to their container image.
Best of all, Kubernetes resources enhanced with OpenShift image streams are still compatible with standard Kubernetes clusters. This fact enables the use of the same resource definitions to support multiple Kubernetes distributions, and at the same time take advantage of features unique to OpenShift.
At the end of this article, we present a few considerations around using image IDs and image name tags to manage your ability to roll back to previous versions of an application.
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In my previous article, Run Red Hat Enterprise Linux 8 in a container on RHEL 7, I showed how to start developing with the latest versions of languages, databases, and web servers available with Red Hat Enterprise Linux 8, even if you are still running RHEL 7. In this article, I’ll build on that base to show how to get started with Node using the current RHEL 8 application stream versions of Node.js and Redis 5.
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In my previous article, Run Red Hat Enterprise Linux 8 in a container on RHEL 7, I showed how to start developing with the latest versions of languages, databases, and web servers available with Red Hat Enterprise Linux 8 even if you are still running RHEL 7. In this article, I’ll build on that base to show how to get started with the Flask microframework using the current RHEL 8 application stream version of Python 3.
From my perspective, using Red Hat Enterprise Linux 8 application streams in containers is preferable to using software collections on RHEL 7. While you need to get comfortable with containers, all of the software installs in the locations you’d expect. There is no need to use
scl commands to manage the selected software versions. Instead, each container gets an isolated user space. You don’t have to worry about conflicting versions.
Continue reading “Develop with Flask and Python 3 in a container on Red Hat Enterprise Linux”
In my previous article, Run Red Hat Enterprise Linux 8 in a container on RHEL 7, I showed how to start developing with the latest versions of languages, databases, and web servers available with Red Hat Enterprise Linux 8 even if you are still running RHEL 7. In this article, I’ll build on that base to show how to get started with Django 2 using the current RHEL 8 application stream versions of Python 3 and PostgreSQL 10.
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Monitoring systems are usually composed of three layers: a database layer that hosts metrics data, a layer to display the stored metric data graphically in dashboards, and an alerting layer to send out notifications via methods such as email, on-call notification systems, and chat platforms. This article presents an overview of the components used in Red Hat OpenShift‘s Application Monitoring Operator, how to install them using the Operator, and an example of the Operator in action.
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We are pleased to announce that Red Hat CodeReady Containers is now available as a Developer Preview. CodeReady Containers brings a minimal, preconfigured OpenShift 4.1 or newer cluster to your local laptop or desktop computer for development and testing purposes. CodeReady Containers supports native hypervisors for Linux, macOS, and Windows 10. You can download CodeReady Containers from the Red Hat CodeReady Containers product page.
CodeReady Containers is designed for local development and testing on an OpenShift 4 cluster. For running an OpenShift 3 cluster locally, see Red Hat Container Development Kit (CDK) or Minishift.
In this article, we’ll look at the features and benefits of CodeReady Containers, show a demo of how easy it is to create a local Red Hat OpenShift 4 cluster, and show how to deploy an application on top of it.
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You can start developing with the latest versions of languages, databases, and web servers available with Red Hat Enterprise Linux 8 even if you are still running RHEL 7. It is pretty simple to do with containers, even if you’ve only been through a “Hello, World” or two.
By the end of this article, you’ll have the current RHEL 8 application stream versions of PHP, MariaDB, and Apache HTTPD running in containers, managed by systemd on your RHEL 7 system. Podman makes it easy to accomplish this since there is no container daemon to complicate things. We’ll use WordPress as a placeholder for your own application code.
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