It leverages specially created Docker images which allow it to do “unusual” things such as run systemd, nested containers etc. Kind is the tool that will create our local Kubernetes cluster.
#CONTROLPLANE ALTERNATIVE FOR MAC INSTALL#
We now have a running Docker server to install our Kubernetes cluster on to. Once it has started, we can check it with: colima listįigure 1: Displays the typical output of Colima’s `list` command Start a Colima VM with: colima start -network-address Colima can also run a basic Kubernetes cluster, but for advanced configurations we will still need Kind. It runs a Lima Linux VM and configures it to host Docker. Install the Docker CLI client with: brew install docker Install and run ColimaĬolima is a lightweight container runtime solution which supports Docker (amongst others). With Colima, we must install it ourselves. If you previously used Docker Desktop, then the CLI tool would have been bundled with it.
A Kubernetes application with a load balancer to test.This solution will install and configure the following components: This article assumes that we are running MacOS 12.6.5 or later, that Docker Desktop is not installed, and the following are already installed: I chose Colima as my replacement primarily because it is lightweight and, as I do not require a GUI, it is a command line only. My original setup used Docker Desktop however, due to licensing requirements, I recently had to find a licence-free alternative. However, with a bit of tinkering, this can also be overcome to create a local Kubernetes cluster that is as close as possible in structure and configuration to a cloud-based setup. This isn’t great, as changing the structure of the application or circumventing a part of it means you never get a true experience when running locally.
#CONTROLPLANE ALTERNATIVE FOR MAC MANUAL#
This means that in order to test your applications, you have to either do a lot of manual port-forwarding directly into pods, and / or introduce “local-only” manifests to define ingress controllers.
Invariably your LoadBalancer objects will never resolve an external IP address. There is no built-in way to deal with this type of resource in a non-cloud-based setup. Whether your production setup is based on AWS, GCP, or Azure, it will almost certainly use LoadBalancer objects which interface with the external cloud infrastructure to create a load balancer outside of the cluster. However, even with Kind, load balancer support is still an issue. So Kind was the starting point for my solution. Apart from Kind, all of the above methods either do not support this, make it too fiddly, or consume excessive resources. and so your local setup should be able to support this. Running multiple nodes is important when you need to test rolling restarts, tolerances, affinity etc. The two main problems I encountered frequently were a) running multiple nodes and b) using load balancers. It can be configured (amongst other things) to create a cluster with multiple control plane and/or worker nodes.Īll of the above methods suffer from a few shortcomings, making it difficult to test a Kubernetes setup without modifying or circumventing part of the system to make it work. There is also Kind (Kubernetes in Docker) which creates a Kubernetes cluster within Docker. Or Docker Desktop ’s built-in Kubernetes cluster, which is limited to a single node. There is Minikube, which creates each node as a VM. There is “the hard way”, where you have to configure VMs manually. There are many ways of running Kubernetes locally. What was needed was a way to get as close as possible to a cloud-based managed cluster on a laptop. Platform engineers also need to test their Kubernetes infrastructure and manifests, and often resort to using dedicated cloud environments to do so, which can be quite expensive. On many of our projects, developers often use docker-compose instead of Kubernetes to test their applications locally, which inevitably causes some friction when deploying them to a cloud environment. Like all cloud-native technologies, Kubernetes can be a challenge to test locally. Considering that the big three cloud vendors (AWS, GCP, and Microsoft Azure) all now offer their own flavour of managed Kubernetes services, it is easy to see how it has become ever more prolific in the “cloud-native architecture” space. As a platform engineer of many years now, Kubernetes has become one of those ubiquitous tools that is simply a must-have in many of our clients’ tech stacks.
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