Kubernetes, often abbreviated as K8s, is an open-source container orchestration platform that automates containerised applications' deployment, scaling, and management. Google initially developed it, and is now maintained by the Cloud Native Computing Foundation (CNCF).
The primary purpose of Kubernetes is to provide a robust and scalable platform for managing containerised workloads. It abstracts the underlying infrastructure, allowing developers and operators to focus on application logic without worrying about the complexities of managing individual containers.
Kubernetes emerged from Google's internal container orchestration system, known as Borg. Google used Borg for over a decade to manage their massive-scale applications. In 2014, Google released Kubernetes as an open-source project, making its powerful orchestration capabilities available to the broader developer community.
Since its inception, Kubernetes has seen rapid adoption and has become a cornerstone of modern cloud-native application development. Its active community and extensive ecosystem of tools and integrations continue to drive its development and innovation.
Kubernetes employs a distributed architecture, allowing for horizontal scalability. This means you can easily add or remove nodes to accommodate varying workloads.
One of the foundational concepts in Kubernetes is the use of containers. Containers provide a lightweight, isolated environment for running applications and their dependencies. They encapsulate the application, its runtime, libraries, and other necessary components, ensuring consistent behaviour across different environments.
In contrast, virtual machines (VMs) virtualise an entire operating system, including its kernel. This leads to a higher resource overhead compared to containers. Kubernetes leverages containers for greater efficiency, allowing for more efficient resource utilisation and faster deployment times.
Container orchestration is automating containerised applications' deployment, scaling, and management. Kubernetes excels at container orchestration by providing powerful features for efficiently managing the lifecycle of containers. It handles tasks such as scheduling containers on nodes, managing networking, and ensuring high availability.
A Kubernetes cluster is a collection of nodes that work together to run containerised applications. It consists of two main components: the master node and worker nodes. The master node manages the control plane, including components like the API server, scheduler, and controller manager. Worker nodes host the actual running containers.
Kubernetes is designed to be a versatile platform that can seamlessly integrate with various cloud providers, including AWS, Google Cloud, and Microsoft Azure. Its architecture is structured around two main components: the master node and worker nodes.
The master node serves as the control centre for the Kubernetes cluster. It coordinates all activities and makes decisions about where to deploy applications. The key components of the master node include:
API Server: This is the entry point for all administrative tasks and serves as the frontend for the Kubernetes control plane. It is accessible via the internet or cloud-specific networks in the case of AWS, Google Cloud, and Microsoft Azure.
Scheduler: Responsible for determining which node should run a specific pod based on resource requirements, constraints, and other factors. It optimizes resource utilization across the cluster, whether it's on AWS, Google Cloud, Microsoft Azure, or other platforms.
Controller manager: Ensures that the desired state of the cluster matches the actual state. It includes controllers for nodes, endpoints, and replication. This ensures uniformity and stability across all major cloud providers.
etcd: A distributed key-value store that stores configuration data for the entire cluster. It serves as the cluster's source of truth, providing consistent and reliable data storage across AWS, Google Cloud, and Microsoft Azure environments.
Worker nodes, or minions, are responsible for running the actual containerized applications. Each worker node consists of:
Kubelet: The primary agent responsible for communicating with the master node. It manages the containers and their lifecycle, ensuring they are in the desired state.
Kube Proxy: Maintains network rules on each node, enabling communication between pods and external network resources. It ensures that network traffic is efficiently routed, regardless of the underlying cloud provider.
Container Runtime: The software responsible for running containers. Common runtimes include Docker, containerd, and others. The choice of container runtime may vary, but Kubernetes abstracts these differences, providing a unified interface.
The control plane is a collection of components that work together to maintain the cluster's desired state. It manages and responds to API requests, ensuring the cluster is in the specified state. The control plane components include the API server, scheduler, controller manager, and etcd.
The architecture of Kubernetes remains consistent across different cloud providers, ensuring that applications can be deployed and managed uniformly. This flexibility allows teams to seamlessly leverage the power of AWS, Google Cloud, Microsoft Azure, or other platforms within the Kubernetes ecosystem.
Understanding the Kubernetes objects mentioned below is crucial for effectively deploying and managing applications within a cluster. They provide different levels of abstraction and functionality to suit various use cases.
A pod is the smallest deployable unit in Kubernetes. It represents a single instance of an application and can contain one or more containers that share resources, including networking and storage. Pods are typically created and managed by controllers.
Services in Kubernetes provide a way to expose a set of pods as a network service. They enable applications to communicate with each other or with external clients. Kubernetes offers various services, including ClusterIP, NodePort, LoadBalancer, and Ingress.
Deployments are a higher-level abstraction that manages the lifecycle of pods. They ensure that a specified number of pod replicas are running at all times, making it easy to scale applications up or down and handle rolling updates.
ConfigMaps allows you to decouple configuration from your containerised application, making it easier to manage configuration data. Conversely, secrets are used for securely storing sensitive information, such as passwords or API tokens.
StatefulSets manage stateful applications, which require stable and unique network identifiers. They provide guarantees about the ordering and uniqueness of pods, making them suitable for databases and other stateful workloads.
DaemonSets ensure that a specific pod runs on all or some selected nodes in the cluster. This is particularly useful for tasks like logging or monitoring agents that need to be present on every node.
Managing the lifecycle of pods is a critical aspect of working with Kubernetes. Understanding how to create, replicate, and update pods ensures the availability and reliability of your applications.
Creating a pod involves defining a pod specification in a YAML file and applying it to the cluster using the kubectl apply command. Deleting a pod can be done using kubectl delete.
Kubernetes provides controllers like ReplicaSets and Deployments, ensuring a specified number of pod replicas are running. This allows for automatic scaling based on resource requirements.
Kubernetes supports rolling updates, which allow you to update an application without downtime. It gradually replaces old pods with new ones. If an update causes issues, Kubernetes supports rollbacks to a previous stable state.
Effective service discovery and load balancing are crucial for ensuring that applications can communicate with each other reliably and efficiently.
Kubernetes provides different types of services to facilitate communication between pods and external clients:
ClusterIP: Exposes a service on an internal IP within the cluster. It's accessible only from within the cluster.
NodePort: Exposes a service on a static port on each node's IP. This allows external clients to access the service.
LoadBalancer: Creates an external load balancer that routes traffic to the service. This is useful for distributing traffic across multiple pods.
Ingress: Manages external access to the services in a cluster. It provides features like SSL termination and virtual hosting.
Kubernetes includes a built-in DNS service allowing pods to discover other services by domain name. This simplifies communication between different components within the cluster.
Kubernetes, often abbreviated as K8s, is an open-source container orchestration platform that automates the deployment, scaling, and management of containerised applications. Google initially developed it, and is now maintained by the Cloud Native Computing Foundation (CNCF).
Kubernetes can be installed using various methods. Popular installation tools include Minikube for local development, kubeadm for setting up multi-node clusters, and managed Kubernetes services provided by cloud platforms like Google Kubernetes Engine (GKE) and Amazon Elastic Kubernetes Service (EKS).
Pods are the most minor deployable units in Kubernetes. They represent one or more containers that share resources, including networking and storage, and are scheduled together on the same node. Pods are the basic building blocks for deploying applications on a Kubernetes cluster.
Kubernetes provides various types of services to facilitate load balancing. These include ClusterIP for internal load balancing, NodePort for exposing services on specific ports on each node, LoadBalancer for external load balancing, and Ingress for managing external access and routing traffic.
Some best practices for deploying applications on Kubernetes include using declarative YAML files to define resources, implementing health checks and readiness probes, utilising ConfigMaps and Secrets for managing configuration data, setting resource requests and limits for pods to optimise resource utilisation, and implementing Horizontal Pod Autoscaling (HPA) for dynamic scaling based on load.
