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)