July 11, 2018
When you design and build applications at scale, you deal with two significant challenges: scalability and robustness. You should design your service so that even if it is subject to intermittent heavy loads, it continues to operate reliably. But how do you build such applications? And how do you deploy an application that scales dynamically? Kubernetes has a feature called autoscaler where instances of your applications are increased or decreased automatically based on metrics that you define.
June 05, 2018
Getting started with Docker and Kubernetes on Windows can be daunting when you don't know where to begin. And it doesn't help that installing the software isn't exactly a walk in the park. In fact, you should already be a Docker and Kubernetes expert to navigate the options on how to install them. But don't worry! If you're just starting your journey with containers and Kubernetes on Windows this article is for you. You'll learn how to make the right choices when it comes to setting up your development environment on Windows.
May 15, 2018
When you deploy an application in Kubernetes, your code ends up running on one or more worker nodes. A node may be a physical machine or VM such as AWS EC2 or Google Compute Engine and having several of them means you can run and scale your application across instances efficiently. When there is an incoming request, the cluster routes the traffic to one of the nodes using a network proxy. But what happens when network proxy crashes? Does the cluster still work? Can Kubernetes recover from the failure?
April 25, 2018
Laravel is an excellent framework for developing PHP applications. Whether you need to prototype a new idea, develop an MVP (Minimum Viable Product) or release a full-fledged enterprise system, Laravel facilitates all of the development tasks and workflows. In this article, I’ll explain how to deal with the simple requirement of running a Laravel application as a local Kubernetes set up.
February 12, 2018
When it comes to building Docker containers, you should always strive for smaller images. Images that share layers and are smaller in size are quicker to transfer and deploy.
But how do you keep the size under control when every
RUN statement creates a new layer, and you need intermediate artefacts before the image is ready?