Continuous Delivery / Deployment with OpenShift Enterprise

In our previous posts, we’ve explored the Red Hat container ecosystem, the Red Hat Container Development Kit (CDK), OpenShift as a local deployment and OpenShift in production. In this final post of the series, we’re going to take a look at how a team can take advantage of the advanced features of OpenShift in order to automatically move new versions of applications from development to production — a process known as Continuous Delivery (or Continuous Deployment, depending on the level of automation).

OpenShift supports

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OpenShift Enterprise in Production

In a previous blog post we took a look at the Red Hat Container Development Kit (CDK) and how it can be used to build and deploy applications within a development environment that closely mimics a production OpenShift cluster. In this post, we’ll take an in-depth look at what a production OpenShift cluster looks like — the individual components, their functions, and how they relate to each other. We’ll also check out how OpenShift supports scaling up and scaling out applications in a production environment.

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Test Driving OpenShift with the Red Hat Container Development Kit (CDK)

Setting up a local development environment that corresponds as close as possible to production can be a time-consuming and error-prone task. However, for OpenShift deployments, we have the Red Hat Container Development Kit (CDK) which does a good job at solving this and also provides a great environment for experimenting with containers and the Red Hat container ecosystem in general.

In this blogpost we will cover deploying applications using the OpenShift Enterprise PaaS that comes with the CDK. The whole process will be driven via the OpenShift CLI, in contrast to our last post which focused on OpenShift’s web interface. If you haven’t yet installed the CDK, check out the previous blog post for instructions.

By the end of this article you will know how to build existing applications on OpenShift, whether they already use

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Looking Back on Containers in 2015

Woah.  2015 went by really quickly.  I do suppose it’s not all that surprising as time flies… especially when you’re having fun or… getting older (you pick).  In fact, we’ve already put 2 percent of 2016 behind us!  That said, before we get too deep into “the future”, and in consideration of Janus having not one but two faces, let’s take a quick trip down memory lane…

Without a doubt, 2015 was an exciting year for all things “container”, especially here at Red Hat.

To recap, the year started off with a bang when we announced the general availability of Red Hat Enterprise Linux Atomic Host alongside Red Hat Enterprise Linux 7.1.  Then – less than two months later

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Announcing OpenShift Enterprise 3.1 and Red Hat Atomic Enterprise Platform Public Preview

This morning, Red Hat announced the general availability of OpenShift Enterprise 3.1 as well as a public preview of Red Hat Atomic Enterprise Platform. Red Hat’s updated container offerings are:

  • OpenShift Enterprise 3.1, the latest version of Red Hat’s application platform designed to build, deploy and run stateful and stateless applications on private and public cloud infrastructure.
  • Red Hat Atomic Enterprise Platform Public Preview, an optimized container infrastructure platform for deploying, running and managing containers across the enterprise.

Both enable enterprises to develop, integrate, deploy, and manage a variety of applications consistently across a more secure, container-optimized infrastructure. If you’re looking to adopt container-based architectures, OpenShift and Atomic allow you to use Docker-formatted Linux containers to create microservices-based applications and modernize traditional workloads – all with the security of a consistent foundation based on Red Hat Enterprise Linux.

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What’s Next for Containers? User Namespaces

What are user namespaces? Sticking with the apartment complex analogy, the numbering of users and groups have historically been the same in every container and in the underlying host, just like public channel 10 is generally the same in every unit in an apartment building.

But, imagine that people in different apartments are getting their television signal from different cable and satellite companies. Channel 10 is now different for for each person. It might be sports for one person, and news for another.

Historically, in the Linux kernel, there was a single data structure which held users and groups. Starting in kernel version 3.8

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