Kubernetes is a popular way to deploy web services and applications using containers. In this, the second of a two-part series, Geshan finishes his step-by-step tutorial for getting up and running with Rails and k8s.
Kubernetes has become very popular in recent years as a way to deploy applications using containers. In this article, Geshan shows us how to get a Rails app up and running inside a local K8s cluster.
Changing the technology an organization works with is a bit like taking up a new sport. Your initial excitement leads you to buy the most expensive equipment you can find, leaving you soon to realize that your new tools have created a steep learning curve. Transitioning out of monolithic applications to microservices is quite similar.
In part 1 of this series on Kubernetes, we discussed how companies like VMware offer the necessary tools to launch, monitor, create and destroy virtual machines. In this post, we review how – much like virtual machines – containers need to be created, monitored, destroyed and relaunched to account for the health of the physical or virtual machines on which they run.
Kubernetes is the leading container-orchestration tool that was open-sourced in 2014 by Google and has helped engineers across the globe to significantly lower their cost of cloud computing ever since. Kubernetes also provides a resilient framework for deploying applications. Kubernetes management tools are quickly becoming essential to those that wish to monitor their containers on an ongoing basis, test, export and create intuitive dashboards.
Docker is a software platform that enables packaging an application into containers. These containers represent isolated environments that provide everything necessary to run the application. Dockerizing an application refers to packaging it in a Docker image to run in one or more containers. Dockerizing an application involves specifying everything needed to run the application in a Dockerfile and then using the file to build a specialized Docker image that can be shared to multiple machines.
