Understanding Kubernetes deployment architectures
There are a multitude of ways to deploy Kubernetes, depending on whether you are developing on your laptop/workstation, deploying to non-production or productions, or whether you are building it yourself or using a managed service such as EKS.
The following sections will discuss how Kubernetes can be deployed for different development environments such as locally on your laptop for testing or for production workloads.
Developer deployment
For local development, you may want to use a simple deployment such as minikube or Kind. These deploy a full control plane on a virtual machine (minikube) or Docker container (Kind) and allow you to deploy API resources on your local machine, which acts as both the control plane and data plane. The advantages of this approach are that everything is run on your development machine, you can easily build and test your app, and your Deployment manifests . However, you only have one worker node, which means that complex, multi-node application scenarios are not possible.
Non-production deployments
In most cases, non-production deployments have a non-resilient control plane. This typically means having a single master node hosting the control plane components (API server, etcd, and so on) and multiple worker nodes. This helps test multi-node application architectures but without the overhead of a complex control plane.
The one exception is integration and/or operational non-production environments where you want to test cluster or application operations in the case of a control plane failure. In this case, you may want to have at least two master nodes.
Self-built production environments
In production environments, you will need a resilient control plane, typically following the rule of 3, where you deploy 3, 6, or 9 control nodes to ensure an odd number of nodes are used to gain a majority during a failure event. The control plane components are mainly stateless, while configuration is stored in etcd. A load balancer can be deployed across the API controllers to provide resilience for K8s API requests; however, a key design decision is how to provide a resilient etcd layer.
In the first model, stacked etcd, etcd is deployed directly on the master nodes making the etcd and Kubernetes topologies tightly coupled (see https://d33wubrfki0l68.cloudfront.net/d1411cded83856552f37911eb4522d9887ca4e83/b94b2/images/kubeadm/kubeadm-ha-topology-stacked-etcd.svg).
This means if one node fails, both the API layer and data persistence (etcd) layers are affected. A solution to this problem is to use an external etcd cluster hosted on separate machines than the other Kubernetes components, effectively decoupling them (see https://d33wubrfki0l68.cloudfront.net/ad49fffce42d5a35ae0d0cc1186b97209d86b99c/5a6ae/images/kubeadm/kubeadm-ha-topology-external-etcd.svg).
In the case of the external etcd model, failure in either the API or etcd clusters will not impact the other. It does mean, however, that you will have twice as many machines (virtual or physical) to manage and maintain.
Managed service environments
AWS EKS is a managed service where AWS provides the control plane and you connect worker nodes to it using either self-managed or AWS-managed node groups (see Chapter 8, Managing Worker Nodes on EKS). You simply create a cluster and AWS will provision and manage at least two API servers (in two distinct Availability Zones) and a separate etcd autoscaling group spread over three Availability Zones.
The cluster supports a service level of 99.95% uptime and AWS will fix any issues with your control plane. This model means that you don’t have any flexibility in the control plane architecture but, at the same time, you won’t be required to manage it. EKS can be used for test, non-production, and production workloads, but remember there is a cost associated with each cluster (this will be discussed in Chapter 2, Introducing Amazon EKS).
Now you’ve learned about several architectures that can be implemented when building a Kubernetes cluster from development to production. In this book, you don’t have to know how to build an entire Kubernetes cluster by yourself, as we will be using EKS.
