A group of nodes is called Kubernetes Cluster together. These clusters run containerized applications. Containerizing applications helps in packaging an application with necessary services and dependencies.
These are lightweight and flexible as compared to virtual machines while allowing easy management and movement of applications. The cluster is considered the heart of Kubernetes.
Kubernetes clusters facilitate containers to efficiently run on multiple machines as well as environments that can be virtual, cloud-based, physical, and on-premises. Unlike virtual machines, Kubernetes containers aren’t just limited to a particular operating system, they can share operating systems and run anywhere.
A cluster consists of a control plane along with computing machines or nodes. The control plane maintains the cluster’s desired state, like which container images are used and the applications that are running. Nodes are useful in running applications and workloads.
Components of a Kubernetes cluster
A Kubernetes cluster consists of 6 components:
- API server – It exposes a REST interface to every Kubernetes resource. This one is often recognized as the front-end of the Kubernetes control plane.
- Controller manager – It runs controller processes and joins the cluster’s original state with its specifications. It also manages various controllers, including endpoints controllers, node controllers, and replication controllers.
- Schedulers – Schedulers help in placing containers as per resource requirements and metrics.
- Kubelet – It ensures that the containers are running within a Pod while interacting with the Docker engine – the program for creating and managing containers.
- Kube-proxy – Controls the network connectivity. Alongside that, it maintains network rules across the nodes. It also helps in implementing the Kubernetes service across nodes in a cluster.
- Etcd – It provides storage for cluster data and is quite consistent.
All these components can be run on Linux or even Docker containers.
How to Work with Kubernetes cluster?
The first and foremost thing to do while working with a Kubernetes cluster is to know its desired state. The desired state includes various operational elements like:
- Applications and workloads that must be run.
- Images that are used by the applications.
- Resources that are needed by the applications.
- Quantity of required replicas.
In order to define the desired state, JSON or YAML files are required to specify the type of application as well as the number of replicas required to run the system.
Developers use Kubernetes API for defining a cluster’s desired state. The command-line interface or API is used to interact with the cluster for manually setting the desired state. Then the master node communicates the desired state to the worker nodes through the API.
The clusters are automatically managed and aligned with their desired state via the Kubernetes control plane. It is responsible for scheduling cluster activity as well as registering along with responding to the cluster events.
Kubernetes control plane manages to run continuous control loops in order to match the cluster’s original state to its desired state.
For example, suppose you’ve deployed an application to run a number of replicas, but one of those replicas crashes. In that case, the Kubernetes control plane will register it and use another replica to maintain the desired state of replicas. Automation occurs through the Pod Lifecycle Event Generator (PLEG). The automatic tasks include:
- Starting and restarting of containers.
- Adjusting the number of replicas to be used on an application.
- Validation of container images.
- Launching and controlling containers.
- Implementation of updates and rollbacks.
How to Create Kubernetes Clusters?
A Kubernetes cluster can be created or deployed on a physical or virtual machine. New users can create clusters through different open-source tools like Minikube. Make sure the tool is compatible with Linux, macOS, and Windows operating systems. Use such a tool to create and deploy a smooth and efficient cluster that consists of just one worker node.
You can use the Kubernetes patterns alongside for automating the management of a cluster’s scale. These allow the reuse of cloud-based architectures for container applications.
Kubernetes offers numerous useful APIs, but it doesn’t provide guidelines to efficiently incorporate the tools in an operating system. Also, Kubernetes patterns offer a consistent way of accessing and reusing Kubernetes architectures.
Conclusion
Using the latest cloud-native apps, Kubernetes have become greatly distributed. These can be deployed on various data centers on-premises, in the public cloud as well as at the edge.
Organizations that wish to use Kubernetes in production or at scale will have several clusters for development, production, and testing, and are distributed among environments, and need to be managed efficiently.