TLDR: This blog post provides an in-depth exploration of Kubernetes architecture, comparing it with Docker, and detailing the components of both the control plane and data plane. It highlights the advantages of Kubernetes, including auto-healing, auto-scaling, and enterprise-level support, while explaining the roles of key components such as the API server, scheduler, etcd, kubelet, and container runtime.
Today marks Day 31 of our free DevOps course, where we will delve into the architecture of Kubernetes. Before we dive into the complexities of Kubernetes architecture, let’s start with a light-hearted question: Why is Kubernetes often referred to as K8s? Feel free to share your thoughts in the comments.
| Docker | Kubernetes | |
| smallest part | container | pod→ (1 or more containers) |
| container runtime | docker shim | kubelet (ensure pod is running, or not) → docker shim / crio / container D |
| networking | Bridge / Docker 0 | kube-proxy (connect multiple pod) → IP tables |
| API Server → take all user req for pod creation and it decide where the pod should be created on availability of worker node | ||
| Scheduler → take action to create pod on available worker node | ||
| etcd → K:V → entire kubernete cluster(node) info | ||
| Controller Manager → running of controllers → Replica Sets (load balancing) → for pods | ||
| Cloud Controller Manager →manage resource(load balancing ,storage) on cloud(AWS/AZURE/GCP) |
The Importance of Understanding Kubernetes
Before we explore Kubernetes architecture, it is crucial to understand the difference between Docker and Kubernetes. If you missed Day 30 of our course, I highly recommend watching that video first. Understanding Docker is essential because Kubernetes is built on top of container orchestration, which enhances the capabilities provided by Docker.
Key Advantages of Kubernetes Over Docker
Kubernetes offers several fundamental advantages over Docker:
Cluster Behavior: Kubernetes is inherently designed to operate in a clustered environment.
Auto Healing: It can automatically replace failed containers.
Auto Scaling: Kubernetes can scale applications up or down based on demand.
Enterprise-Level Support: It provides advanced load balancing, security features, and networking capabilities.
Overview of Kubernetes Architecture
Kubernetes architecture can be broadly divided into two main components: the Control Plane and the Data Plane. While many resources explain these components, understanding their roles in relation to Docker can provide deeper insights.
Control Plane vs. Data Plane
Control Plane: This is the brain of Kubernetes, managing the cluster and making decisions about the deployment and scaling of applications.
Data Plane: This is where the actual workloads run, consisting of the nodes that execute the applications.
Components of Kubernetes Architecture
Data Plane Components (Worker node)
In the data plane, we primarily focus on three components:
Kubelet: This agent runs on each worker node and is responsible for managing the pods. It ensures that the pods are running as expected and communicates with the control plane.
Kube Proxy: This component manages network routing for the pods, providing load balancing and ensuring that each pod has a unique IP address.
Container Runtime: This is the software responsible for running the containers. Kubernetes supports various container runtimes, including Docker, containerd, and CRI-O.
Control Plane Components (Master node)
The control plane consists of several key components:
API Server: This is the central management entity that exposes the Kubernetes API. It handles all requests from users and other components.
Scheduler: The scheduler is responsible for assigning pods to nodes based on resource availability and other constraints.
etcd: This is a distributed key-value store that holds all cluster data, including configuration data and the state of the cluster.
Controller Manager: This component manages controllers that regulate the state of the cluster, ensuring that the desired state matches the actual state.
Cloud Controller Manager: This component interacts with cloud service providers to manage resources such as load balancers and storage.
Comparing Docker and Kubernetes
To better understand Kubernetes, let’s compare it with Docker:
In Docker, the simplest unit is a container, while in Kubernetes, it is a pod. A pod can encapsulate one or more containers, providing additional capabilities such as shared networking and storage.
When a container is created in Docker, it requires a container runtime to execute. Similarly, Kubernetes requires a container runtime, but it offers flexibility by supporting multiple runtimes.
Example of Pod Creation in Kubernetes
When a user deploys a pod in Kubernetes, the request goes through the control plane. The kubelet on the worker node is responsible for ensuring that the pod is running. If the pod fails, the kubelet informs the control plane to take corrective actions, leveraging Kubernetes' auto-healing capabilities.
Conclusion
In summary, Kubernetes architecture is designed to provide robust, scalable, and resilient application deployment and management. Understanding the roles of the control plane and data plane components is essential for anyone looking to work with Kubernetes effectively.
As an assignment, I encourage you to write detailed notes on this topic and share them on LinkedIn. This will not only reinforce your understanding but also showcase your knowledge to potential employers.
If you have any questions or need clarification on any component, please leave a comment, and I will be happy to assist. Thank you for watching, and I look forward to seeing you in the next video, where we will explore Kubernetes Pods in more detail.