In an earlier blog post, Kubernetes Overview, we introduced Kubernetes. We introduced key capabilities of Kubernetes i.e. Portability, Extensibility, Declarative Configuration and Automation.
Let’s take a closer look at these capabilities to acquire deeper insights into the technology.
Portability
The portability of Kubernetes refers to its ability to enable consistent and seamless deployment and management of containerized applications across diverse computing environments. This portability is crucial for organizations seeking to avoid vendor lock-in, optimize resource utilization, and adapt to changing business needs.
Kubernetes portability includes:
- Container Image Portability: Kubernetes is designed to work with container images, which are inherently portable. This means that applications packaged as containers can be easily moved between different Kubernetes clusters without modification.
- Consistent Application Behavior: Regardless of the underlying infrastructure, Kubernetes ensures consistent application behavior by abstracting the complexities of different environments, such as networking, storage, and compute resources.
- Multi-Cloud Compatibility: Kubernetes supports deployment on major public cloud providers like AWS, Azure, and Google Cloud, allowing applications to move easily between different cloud environments.
- On-Premises Deployments: Kubernetes is not limited to the cloud; it can be deployed on on-premises hardware, providing flexibility for organizations that choose to maintain their infrastructure.
- Hybrid Cloud Environments: Organizations can leverage Kubernetes to manage applications seamlessly in hybrid cloud setups, combining both on-premises and cloud resources.
Extensibility
The extensibility of Kubernetes refers to its ability to support integration of additional features to the cluster without altering the core source code, offering flexibility and adaptability to diverse application needs without significant changes.
Kubernetes extensibility includes:
- Custom Resource Definitions (CRDs): Kubernetes allows users to define custom resource types and their behaviors through CRDs. This feature enables the extension of the Kubernetes API to support new object types specific to an organization or application.
- Custom Controllers: Users can develop custom controllers to extend Kubernetes’ control loop, allowing the automation of specific tasks or workflows tailored to unique business needs.
- Admission Controllers: Kubernetes supports admission controllers that intercept and modify admission requests to the API server. This enables users to enforce custom policies and validations during the resource creation process.
- API Aggregation: Kubernetes provides API aggregation layers that allow users to expose additional APIs without modifying the core components. This facilitates the integration of external services and functionalities seamlessly.
- Plug-ins and Extensions: Kubernetes features a plugin architecture that enables the integration of various networking, storage, and authentication solutions. Users can select and integrate plug-ins based on their specific needs.
- Operators: Kubernetes Operators are a framework for packaging, deploying, and managing applications on Kubernetes. They encapsulate operational knowledge and automate complex application management tasks.
- Container Runtimes: While Kubernetes supports the Docker container runtime by default, it is extensible to other container runtimes, such as containerd or CRI-O, allowing users to choose the runtime that best suits their requirements.
- Webhooks: Kubernetes supports the use of webhooks for triggering custom actions in response to specific events, providing a mechanism for extending and customizing behavior at runtime.
Declarative configuration
Declarative configuration management in Kubernetes refers to the practice of specifying the desired state of a system or application through configuration files, without explicitly detailing the steps needed to achieve that state. Declarative configuration management simplifies the practice of deployment and management of applications on Kubernetes. Configuration details are typically expressed in YAML or JSON format, providing a human-readable and machine-friendly representation of the desired state.
Key concepts of Declarative Configuration include:
- Desired State Specification: Users declare the desired state of their applications or infrastructure by describing the configuration parameters, relationships, and characteristics of Kubernetes resources in a declarative manner.
- Automatic Reconciliation: Kubernetes controllers continuously monitor the actual state of the system and reconcile any differences with the declared state. This ensures that the system remains in the desired state, automatically handling updates, scaling, and recovery.
- Immutability and Idempotency: Declarative configurations promote immutability, meaning that once a configuration is set, it should not be changed directly. Changes are made by updating the configuration files, and Kubernetes ensures idempotency, where applying the same configuration multiple times produces the same result.
- Kubernetes API Objects: Configuration files define Kubernetes API objects, such as Deployments, Services, ConfigMaps, and more. Each object represents a part of the application or infrastructure, and the relationships between these objects define the system architecture.
- Rolling Updates: Declarative configuration enables rolling updates by allowing users to specify a new version of their application in the configuration files. Kubernetes orchestrates the update process, ensuring minimal downtime and a smooth transition to the new state.
- Secret and Configuration Management: It offers a secure mechanism to store and manage sensitive information like passwords, tokens, and keys. Kubernetes enables deployment and updating of secrets and configurations without exposing them or requiring container image rebuilding.
Automation:
Automation in Kubernetes refers to the ability of the system to perform tasks, manage resources, and respond to events without manual intervention. Automation in Kubernetes streamlines the management of containerized workloads, reduces manual intervention, enhances efficiency, and contributes to the resilience and scalability of applications deployed on the platform.
Key concepts of Automation in Kubernetes include:
- Automated Rollouts and Rollbacks: By defining desired states for deployed containers, Kubernetes automates the transition from actual to desired states at controlled rates. It dynamically creates, removes, or adopts resources to ensure smooth deployments and rollbacks.
- Automatic Bin Packing: Kubernetes optimizes resource utilization by intelligently assigning containers to nodes based on specified CPU and memory requirements, effectively utilizing cluster resources.
- Scaling Automation: Kubernetes supports automatic scaling based on resource utilization or custom metrics. Horizontal Pod Autoscaling (HPA) dynamically adjusts the number of pod replicas to maintain optimal performance, responding to changes in demand.
- Load Balancing: Kubernetes automates the distribution of network traffic to application instances through built-in load balancing mechanisms. Services can automatically discover and load balance traffic to healthy pods, ensuring high availability and efficient resource utilization.
- Rolling Updates and Rollbacks: Kubernetes automates the process of updating applications by gradually replacing old container instances with new ones, ensuring minimal downtime. If issues arise during an update, Kubernetes supports automatic rollbacks to the previous stable version.
- Event-Driven Automation: Kubernetes supports event-driven automation through mechanisms like controllers and webhooks. Controllers watch for changes in the cluster and automatically trigger actions or responses based on predefined rules.
- Cluster Operations: Kubernetes continuously monitors the health of applications using user-defined health checks. Unhealthy instances are automatically restarted, replaced, or removed from the load balancing pool, contributing to a self-healing system.
- Storage Orchestration: It enables automatic mounting of various storage systems, whether local or from public cloud providers, simplifying the management and utilization of storage resources.
- Network Orchestration: Automating networking tasks to ensure seamless communication between pods, along with the implementation of network policies for controlled communication.