Kubernetes (K8s) RoadMap: Beginner Start these Topics  Basics to Advanced

Kubernetes (K8s) RoadMap: Beginner Start these Topics Basics to Advanced

1. Introduction to Kubernetes

  • What is Kubernetes?
  • The Need for Container Orchestration
  • Key Features and Benefits of Kubernetes

2. Core Concepts of Kubernetes

  • Cluster Architecture Overview:Control Plane (Master Node): Components and functionsWorker Nodes (Slave Nodes): Components and roles
  • Pods: The smallest deployable units in K8s
  • Services: Networking in Kubernetes (ClusterIP, NodePort, LoadBalancer, Headless Services)
  • Deployments: Managing stateless applications
  • Namespaces: Organizing Kubernetes objects
  • Labels and Selectors: Identifying and grouping Kubernetes resources
  • Annotations: Storing metadata in objects

3. Kubernetes Architecture

  • Control Plane Components:API Server: Frontend to the Kubernetes control planeEtcd: Key-value store for all cluster dataController Manager: Manages controllers (ReplicaSet, Deployment, etc.)Scheduler: Determines which nodes the Pods should run on
  • Worker Node Components:Kubelet: Agent that runs on each worker nodeKube-Proxy: Network proxy and load balancerContainer Runtime: Software that runs containers (e.g., Docker, containerd)
  • Sidecar Containers: Patterns for extending application functionality

4. Setting Up a Kubernetes Cluster

  • Local Development Setup: Minikube, Kind
  • Production Cluster Setup: Using cloud providers (GKE, EKS, AKS)
  • Kubeadm: Manual cluster setup
  • Managed Kubernetes Services: Overview of managed services (Google Kubernetes Engine, Amazon EKS, Azure AKS)

5. Kubernetes Objects and Configurations

  • ConfigMaps: Managing configuration data
  • Secrets: Managing sensitive information
  • Persistent Volumes (PV) and Persistent Volume Claims (PVC): Storage management in Kubernetes
  • StatefulSets: Managing stateful applications
  • DaemonSets: Running a daemon on all or some nodes
  • Jobs and CronJobs: Batch processing and scheduled tasks
  • Custom Resources (CR) and Custom Resource Definitions (CRD): Extending Kubernetes capabilities
  • Service Accounts: Authentication within clusters
  • Node Affinity and Node Selectors: Controlling Pod placement on nodes
  • Taints and Tolerations: Controlling Pod scheduling

6. Kubernetes Networking

  • Cluster Networking: How Pods communicate internally
  • Network Policies: Securing network traffic between Pods
  • Ingress Controllers: Managing external access to services
  • Service Meshes: Advanced traffic management (Istio, Linkerd)
  • CNI (Container Network Interface): Plugins and network configuration
  • Headless Services: Direct access to Pods

7. Kubernetes Security

  • RBAC (Role-Based Access Control): Managing access and permissions
  • Cluster Roles and Role Bindings: Defining permissions across the cluster
  • Pod Security Policies: Controlling security-sensitive aspects of Pod specification
  • Network Policies: Defining rules for network traffic
  • Image Security: Best practices for securing container images
  • Secrets Management: Securely managing sensitive data
  • Admission Controllers: Security and policy enforcement
  • Service Accounts: Managing credentials and access within clusters
  • Authentication and Authorization: Integrating external authentication mechanisms (OIDC, LDAP)

8. Kubernetes API and Extensibility

  • Kubernetes API Server: Interacting with the Kubernetes API
  • API Aggregation Layer: Extending the Kubernetes API
  • Admission Controllers: Modifying or rejecting requests to the API server
  • Custom Controllers and Operators: Automating cluster management tasks
  • Webhooks: Customizing admission and mutation logic
  • API Programming: Using client libraries and tools to interact with the Kubernetes API

9. Scaling and Self-Healing in Kubernetes

  • Horizontal Pod Autoscaler (HPA): Autoscaling Pods based on CPU/memory usage
  • Vertical Pod Autoscaler (VPA): Autoscaling Pods based on resource requests/limits
  • Cluster Autoscaler: Automatically adjusting the size of a Kubernetes cluster
  • Self-Healing Capabilities: Restarting failed containers, replacing and rescheduling Pods when nodes die
  • Scaling Stateful Applications: Managing StatefulSets and DaemonSets scaling
  • Multi-Cluster Scaling: Federated clusters and cross-cluster scaling

10. Monitoring and Logging

  • Prometheus and Grafana: Setting up monitoring and alerting
  • ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging solution
  • Fluentd and Fluent Bit: Log forwarding and aggregation
  • Sidecar Pattern for Logging and Monitoring: Using sidecars for log management
  • Application Performance Management (APM) Tools: Integrating APM tools for better insights

11. Kubernetes Operators

  • What are Kubernetes Operators?
  • Building and deploying Operators
  • Managing complex applications with Operators
  • Operator SDK and Helm Operator
  • Commonly Used Operators: Prometheus Operator, MySQL Operator, etc.

12. Helm: Kubernetes Package Management

  • Introduction to Helm
  • Helm Charts: Creating and deploying
  • Using Helm for managing applications
  • Advanced Helm Usage: Helmfile, Templating, and Repositories

13. Advanced Kubernetes Concepts

  • Sidecar and Ambassador Containers: Extending and enhancing applications
  • Init Containers: Initializing application states
  • Multi-Cluster Management: Managing multiple Kubernetes clusters
  • Service Mesh Integrations: Advanced traffic management and observability
  • Kubernetes Federation: Managing multiple clusters across regions
  • Serverless Kubernetes: Using K8s for serverless workloads (Knative, Kubeless)
  • Air-Gapped Kubernetes Clusters: Running K8s in isolated environments
  • Kubernetes in Edge Computing: Using K8s for edge deployments

14. Resource Management in Kubernetes

  • Resource Requests and Limits: Managing CPU and memory resources
  • Quotas and LimitRanges: Controlling resource usage in namespaces
  • Node Resource Management: Optimizing and managing node resources

15. Troubleshooting and Best Practices

  • Common Kubernetes issues and troubleshooting steps
  • Kubernetes best practices for production environments
  • Performance optimization and resource management
  • Debugging tools and techniques (kubectl debug, strace, etc.)
  • Backup and Restore: Ensuring data and state safety
  • Disaster Recovery Planning: Strategies for cluster recovery

16. Kubernetes Ecosystem and Tools

  • Overview of Kubernetes-related tools (kubectl, k9s, kubeadm, etc.)
  • Using CI/CD with Kubernetes (Jenkins, ArgoCD, Tekton)
  • Kubernetes IDEs and Dashboards (Lens, Octant, etc.)
  • GitOps: Continuous delivery through version control (ArgoCD, Flux)
  • Kubernetes IDEs: Developing and managing Kubernetes workloads

17. Conclusion and Next Steps

  • Recap of Kubernetes Core Concepts
  • Advanced Topics for Further Study
  • Community and Resources for Continuous Learning
  • Certifications and Career Paths: CNCF certifications (CKA, CKAD, CKS)

AARYAN GUPTA

Devops Engineer | 3xRedHat Certified | 1xGCP | Generative AI | AWS | Docker | Kubernetes | Ansible | Terraform | IOT/Python Developer | IOT & Robotics Instructor {Trained 500+ National/International Students}

6 个月

Thanks for sharing Ratan shakya

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