Kubernetes is a hot phrase in the world of cloud technologies right now. If you are a developer, you have no doubt heard of the technology, but may be a little confused by the concept, as Kubernetes can be one of the hardest containerizing technology architects to understand. To that end, this containerization tutorial will seek to answer the question: What is Kubernetes? We will also discuss the difference between Kubernetes and Docker.
Kubernetes Basics and Advantages
Kubernetes is a type of modern cloud and microservices software, specifically in the field of containerization coordination and management processes. To make things more clear, we can envision that, if the container is a tree, then Kubernetes is a forest that contains tens or even thousands of various kinds of trees (or containers). This means that Kubernetes is an orchestrator for containers, such as Docker, across a distributed cluster of container nodes.
Kubernetes is an open-source platform, created by Google using the Go (or Golang) programming language. With Google’s previous backlog of container orchestration experience, Kubernetes has become the de facto provider of REST APIs for containerization functionalities in the industry. Also, it has become one of the most celebrated native cloud application platforms.
Kubernetes’ advantages become very necessary when you actually have a huge number of complex running containers. Some of the advantages of Kubernetes for developers include:
- Supports containerizing for large amounts of applications and development projects.
- Getting a 24/7 level of availability for applications to enable developers to deploy apps at a quicker rate and also provides load balancing.
- Allow applications to run at any time with nearly zero downtime.
- Help in the form of resources and tools that applications need to work properly.
- Widespread support community across global cloud platforms.
- Provisions automatic scaling (up and down) of containers and software development lifecycle (SDLC) management.
- Unifies container orchestration operations based on determined global standard criteria.
Performs health checks periodically for applications and auto-fixing failures (self-healing).
- Provides an extensible premier platform to enhance container configuration.
- Growing ecosystem that supports wide available tools and projects from a variety of companies and industries.
- Facilitates declarative management to ensure the observed state matches the desired state.
- Provides a resilient model to keep open options for users to choose what suits them.
- Portability to run on any private, hybrid, or public cloud across all cloud platforms.
- Automatic binary through packaging the application and placing containers according to requirements.
- Automatic load balancing with assigning IP addresses and DNS names for containers.
- Storage coordination via an auto mounting between various storage systems.
- Rollback and rollout updates automatically by applying or postponing them based on availability.
The most difficult aspect to understand in Kubernetes is its architecture. However, you don’t need to fully digest everything here. For our purposes, it is enough to take a glimpse of the main Kubernetes components and their functions. The most important thing is to know that Kubernetes is a management platform for containers.
In Kubernetes architecture, there is a master server (or more than one for best availability). If there is more than one, one of the servers must be a master server for controlling nodes. Kubernetes is based on the client-server architecture software model. The controlling node or master server consists of:
- Kube-Episerver: A central management component that treats all REST requests.
- Etcd storage: A distributed key-value storage that stores the Kubernetes cluster data.
- Kube-controller-manager: Runs many distinct controller processes in the background.
- Cloud-controller-manager: Manages controller processes on the underlying cloud provider.
- Kube-scheduler: Schedules the pods on the various nodes based on resource utilization.
- DNS server: Configures the individual containers using the DNS and schedules the DNS Pod on the cluster.
There is also the Kubernetes Cluster, which includes a collection of worker nodes in addition to the master node. It can be run on multiple nodes in the case of production environments or on the same node in case of testing purposes by using minikube.
The functioning of a cluster in general consists of these components:
- API server: Runs all of the administrative tasks on the master node.
- Scheduler: Schedules the work between many worker nodes.
- Controller manager: Control loops that regulate the Kubernetes clusters state.
- Kubelet: An agent on each node that checks the containers running in the pods.
- Kube-proxy: A network proxy that contains all the rules of nodes in a cluster.
- Etcd: Manages the cluster state.
There are also more other important terms or components associated with Kubernetes, such as:
- Pods: One or more containers running together in a cluster.
- Namespace: Supports multi virtual clusters in one physical cluster.
- Nodes: The machines that are running Kubernetes.
- Cluster: A group of machine nodes running applications on Kubernetes.
- Volume: A data directory accessed by pods.
- ReplicaSet: A number of replicas of running pods.
- Kubectl: A command-line tool for calling with the Kubernetes API server.
- Worker Node: A virtual or physical pod server controlled by the master node.
Kubernetes versus Docker
In the chaos of learning the basics of these technologies, terms, and brands, it is very common to get confused about the differences between containers, Docker, and Kubernetes. It is true that Docker is container software, but Kubernetes is software for managing a lot of containers.
The container is a platform software to package OS, libraries, and required applications inside a virtual container. There is no real comparison between Docker and Kubernetes except to clarify the confusion that naturally occurs a lot these days with every new term of cloud, artificial intelligence (AI), and database computing.
Working in Docker is only suitable when you have a small number of containers that you can keep control of manually, while Kubernetes is used for cases that consist of many pods that have containers. As you read above, Kubernetes keeps the control in an automated way for all the container cluster needs.