Managing the Lifecycle of OpenShift Clusters: Vetting OpenShift Installations

Managing the Lifecycle of OpenShift Clusters: Vetting OpenShift Installations

Whether installing a new release of a software package or just installing an update (such as a bug fix), it is wise to perform tests against the newly installed software in order to confirm that it is performing correctly in the target environment. This is especially true with OpenShift since it contains a number of open source components and can be deployed to a variety of environments, such as an on-prem datacenter, or a public or private cloud.

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Kubernetes Services By Example

Kubernetes Services By Example

When explaining Kubernetes to people new in the space I noticed that the concept of services is often not well understood. To help you better understand what services are and how you can troubleshoot them, we will have a look at a concrete setup and discuss the inner workings of services in this post.

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Enhancing your Builds on OpenShift: Chaining Builds

Enhancing your Builds on OpenShift: Chaining Builds

In addition to the typical scenario of using source code as the input to a build, OpenShift build capabilities provides another build input type called “Image source”, that will stream content from one image (source) into another (destination).

Using this, we can combine source from one or multiple source images. And we can pass one or multiple files and/or folders from a source image to a destination image. Once the destination image has been built it will be pushed into the registry (or an external registry), and will be ready to be deployed.

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Jupyter on OpenShift Part 5: Ad-hoc Package Installation

Jupyter on OpenShift Part 5: Ad-hoc Package Installation

The main reason persistent volumes are used is to store any application data. This is so that if a container running an application is restarted, that data is preserved and available to the new instance of the application.

When using an interactive coding environment such as Jupyter Notebooks, what you may want to persist can extend beyond just the notebooks and data files you are working with. Because it is an interactive environment using the dynamic scripting language Python, a user may want to install additional Python packages at the point they are creating a notebook.

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Jupyter on OpenShift Part 4: Adding a Persistent Workspace

Jupyter on OpenShift Part 4: Adding a Persistent Workspace

To provide persistence for any work done, it becomes necessary to copy any notebooks and data files from the image into the persistent volume the first time the image is started with that persistent volume. In this blog post I will describe how the S2I enabled image can be extended to do this automatically, as well as go into some other issues related to saving of your work.

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Containers are Linux

Containers are Linux

Containers are Linux. The operating system that revolutionized the data center over the past two decades is now aiming to revolutionize how we package, deploy and manage applications in the cloud. Of course you’d expect a Red Hatter to say that, but the facts speak for themselves.

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What’s New in OpenShift 3.5 – Enhanced Usability

What's New in OpenShift 3.5 - Enhanced Usability

The team continues to process feedback and turn it into improvements to the experience of OpenShift, the 3.5 release is no different. There are too many to list in this single blog post, so we’ll highlight a few here such as: “create from URL”, improved feedback messages, more kubernetes resources support and pipeline samples.

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Jupyter on OpenShift Part 2: Using Jupyter Project Images

Jupyter on OpenShift Part 2: Using Jupyter Project Images

The quickest way to run a Jupyter Notebook instance in a containerised environment such as OpenShift, is to use the Docker-formatted images provided by the Jupyter Project developers. Unfortunately the Jupyter Project images do not run out of the box with the typical default configuration of an OpenShift cluster.

In this second post of this series about running Jupyter Notebooks on OpenShift, I am going to detail the steps required in order to run the Jupyter Notebook software on OpenShift.

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Jupyter on OpenShift: Using OpenShift for Data Analytics

Jupyter on OpenShift: Using OpenShift for Data Analytics

It is a commonly used catch phrase to say how ‘Software is Eating The World’ and how all companies are now software companies. It isn’t just the software that is important though, it is the data which is being generated by these systems. At the extreme end of the spectrum, companies can generate or collect quite massive data sets, and this is often referred to as the realm of ‘Big Data’.

No matter how much data you have, it is of no value if you don’t have a way to analyse the data and visualise the results in a meaningful way that you can act upon.

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