In this post, let’s check 5 Kubernetes mini-projects that use a variety of resources and offer unique scenarios to make better understanding (where to use Kubernetes?)
Project 1: Scalable Web Application
- Requirement: Create a highly available website that can handle varying traffic loads.
- Summary: Deploy a containerized web application, use load balancing for distribution, and configure autoscaling rules to handle surges in requests.
- Tools/Services:
- Docker (or another container engine) for building container images.
- Kubernetes Services for load balancing.
- Horizontal Pod Autoscalers for automatic scaling.
Project 2: Deploy a Self-Healing Microservice
- Requirement: Ensure a microservice is always available, even in case of pod failures.
- Summary: Deploy a microservice with multiple replicas, utilize health checks (liveness and readiness probes), and set up automatic redeployment of failed pods.
- Tools/Services:
- Docker for building images.
- Kubernetes Deployments to manage multiple replicas.
- Kubernetes Liveness & Readiness probes for health monitoring.
Project 3: Database with Persistent Storage
- Requirement: Deploy a database that retains data even if pods are rescheduled.
- Summary: Provision a database (e.g., PostgreSQL, MySQL), and attach persistent storage to hold data securely.
- Tools/Services:
- Docker for containerizing the database.
- Kubernetes Persistent Volumes & Persistent Volume Claims for storage management.
- A database of your choice.
Project 4: CI/CD Pipeline with Jenkins
- Requirement: Automate build, test, and deployment of a containerized app.
- Summary: Set up Jenkins within Kubernetes and create a pipeline that pulls code changes, builds a new container image, tests it, and deploys the update to an environment.
- Tools/Services:
- Docker for building container images.
- Jenkins deployed as a Kubernetes Pod.
- A source code repository (e.g., GitLab, GitHub).
Project 5: Centralized Logging with Fluentd
- Requirement: Collect and store log data from all the containers in your Kubernetes cluster.
- Summary: Deploy Fluentd as a DaemonSet to gather logs from containers, and ship them to a storage and analysis platform like Elasticsearch.
- Tools/Services:
- Fluentd deployed in Kubernetes.
- Elasticsearch (can be outside Kubernetes) for log storage and analysis.
- Kibana (optional) for log visualization.
Important Considerations:
- Start Small: Begin with basic versions of each project and gradually increase complexity.
- Cloud/Local Setup: You can use cloud-based Kubernetes clusters (AWS EKS, GCP GKE, Azure AKS) or local solutions like Minikube for experiments.
- Focus on Concepts: While tools are important, emphasize understanding core Kubernetes resources (Deployments, Services, ConfigMaps, Secrets, etc.) within each project.
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Hope you find this post helpful.
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