When you are using Kubernetes, it provides a cluster.
A Kubernetes cluster includes a collection of worker systems known as nodes that execute containerized apps. Each cluster has a minimum of one worker node.
The worker node(s) host or store the Pods that make up the application workload. The control plane supervises the cluster’s worker nodes as well as Pods. In production conditions, the control plane typically distributes across many systems, and a cluster distributes across numerous nodes to provide fault resilience and high accessibility.
This blog describes the many components required for a fully functional Kubernete cluster.
Control Plane Components
The components of the control plane make global choices about the cluster (such as scheduling), as well as monitor & react to cluster events.
Control plane components can be executed on any cluster computer. Setup scripts, on the other hand, often launch all control plane components on the same system and do not execute user containers on this system for simplicity.
The API server is a component that exposes the Kubernetes API. The API server serves as the front end for the Kubernete control plane.
kube-apiserver is the core Kubernete API server implementation. kube-apiserver intends to scale horizontally, which means it deploys new instances. You may run many instances of kube-apiserver & balance traffic among them.
Kubernetes’ backup store for all cluster data is a uniform and highly accessible key value store. If your Kubernetes cluster relies on etcd as its backing store, ensure that you have a backup strategy in place.
Control plane component that looks for newly formed Pods that have no assigned node and chooses a node for them to run on.
Individual as well as collective resource needs, hardware/software/policy limitations, affinity & anti-affinity requirements, data localization, inter-workload interference, plus deadlines are among the factors considered in scheduling decisions.
Controller processes are run by the control plane component. Each controller is a distinct process logically, but they are all built into one binary and operated in a single process to decrease complexity.
These controllers are classified as – Node controller, job controller, endpoints controller, and service account & token controllers.
It’s a Kubernetes control plane component that involves cloud-specific control logic. The cloud controller manager allows you to connect the cluster to the cloud provider’s API and also separates components that engage with the cloud platform from those that just interact with the cluster.
Only controllers relevant to the cloud provider are managed by the cloud-controller-manager. When you deploy Kubernete on your site or in a learning context on your PC, the cluster lacks a cloud controller manager.
The cloud-controller-manager, like the kube-controller-manager, integrates numerous conceptually distinct control loops into a unified binary that runs as a single process. You may scale horizontally (run several copies) to boost performance or endure errors.
Every node has node components that keep pods operating and provide the Kubernetes execution environment.
An agent that operates on each cluster node. It ensures that containers in a Pod are operating. The kubelet accepts a collection of PodSpecs delivered in various ways and guarantees that the containers defined in those PodSpecs are up and running. Containers that were not generated by Kubernete are not managed by the kubelet.
kube-proxy is a network proxy that works or runs on every node in the cluster and implements a component of the Kubernetes Service model. kube-proxy keeps network rules up to date on nodes. These network constraints let network connectivity to the Pods through network sessions both inside and outside of the cluster.
If an operating system packet filter layer is present, kube-proxy utilizes it. Alternatively, kube-proxy sends the traffic.
The container runtime is the program that is in charge of container execution.
Kubernetes allows container runtimes or latency like containerd, CRI-O, or any other Kubernetes CRI version (Container Runtime Interface).
Addons implement cluster functionalities by utilizing Kubernetes features (DaemonSet, Deployment, and so on). Namespaced elements for addons reside in the kube-system namespace since they provide cluster-level functionalities.
Selected addons are mentioned here –
While the remaining add-ons are optional, every Kubernetes cluster should include cluster DNS because many instances rely on it. Cluster DNS is a DNS server that delivers DNS records for Kubernetes services in addition to the other DNS server(s) in the environment. Containers launched by Kubernetes utilize this DNS server in their DNS queries by default.
Web UI – Dashboard
The dashboard is a web-based user interface for Kubernetes clusters. It enables users to manage and debug clustered apps and the cluster itself.
Container Resource Monitoring
Container Resource Monitoring stores general time-series information about containers in a unified database and offers a user interface for exploring that data.
Container logs are recorded in a central log store with a search or browsing interface through a cluster-level logging system.
Kubernetes Cluster Architecture Best Practices
The following practices can help you in building productive Kubernetes clusters:
- Version updates — Always use the most recent version of Kubernetes.
- Educate teams — Invest in training DevOps teams in advance. Provide them with Kubernetes training.
- Manage integration — Standardise governance throughout the company to guarantee that all suppliers and products are in sync and correctly linked with Kubernetes.
- Scan Images — Image scanners should be integrated into the CI/CD workflow, including throughout the build & run cycles. Open source code via Github repositories should always be regarded with caution.
- Control access — Enforce the least privilege principle & zero-trust models by implementing role-based access control (RBAC) throughout all clusters.
- Restriction users — Use just the non-root users & make the file system read-only.
- Minimalist base images — Basic Docker Hub images may include viruses or any other unwanted programming. When feasible, begin with clean and lean code, and then work your way up. Small pictures, in general, generate faster & take up less disc space.
- Simplify containers — Establish a single process for every container. This makes it easy for the orchestrator to assess & report on the process’s health.
- Be descriptive — Descriptive labels can assist other developers & stakeholders know the Kubernetes cluster configuration and procedures.
- Prevent over-granularity — Not every function in a logical code component is to be treated as a distinct microservice.
- Automate — By automating the CI/CD workflow, you can completely avoid manual Kubernetes deployments.
- Control pods — Use readinessProbe & livenessProbe to administer pod lifecycles. Pods can be disabled during setup if they receive user requests too soon.
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Our Kubernetes training includes sessions from the foundations to advanced topics of Docker and Kubernetes. This Kubernetes certification course enables you to interact with industry professionals, develop your skills and capabilities to satisfy industry as well as organizational standards, and learn about real-world best practices.
This Kubernetes Certification course covers the following –
- Essentials of Docker
- Overview of Kubernetes
- Kubernetes Cluster
- Overview Kubernetes Pod
- Kubernetes Client
- Creating and modifying ConfigMaps and Secrets
- Replication Controller and Replica Set
- Exploring the Kubernetes API and Key Metadata
- Managing Specialized Workloads
- Volumes and configuration Data
- Monitoring and logging
- Maintenance and troubleshooting
- The ecosystem
Prerequisites for Docker & Kubernetes Certification
- Basic command knowledge of Linux
- Basic understanding of DevOps
- Basic knowledge of YAML programming language (beneficial, but not mandatory)