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)
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