🌩️ Elevate the Server to Cloud Readiness: Dockerization, Kubernetes, and Monitoring 🚀

[ajaynegi45]

Problem

Our current HTTP server, while functional and robust for on-premise and local deployments, lacks the critical infrastructure needed to be cloud-ready. In today’s cloud-driven world, we need to prepare the server for seamless deployment, scaling, and monitoring in cloud environments to ensure it can handle high traffic and maintain performance.

Why This Matters

To ensure the server is lightweight, scalable, and capable of handling real-world workloads in cloud environments, we must make it adaptable to cloud-native principles. This means the server should be easy to deploy using containers, support auto-scaling via orchestration tools like Kubernetes, and offer monitoring and observability to track performance metrics. This issue will focus on introducing essential features like Dockerization, Kubernetes orchestration, and performance monitoring.

Goals

We need contributors to implement cloud-readiness for the server. Here are the key tasks involved:

1. Dockerization:

   • Package the HTTP server into a Docker container.
   • Ensure it is easy to deploy in any cloud environment by providing a Dockerfile with clear instructions on how to build and run the server.
   • Make sure that the server runs smoothly inside the container.
   • The final Docker image should be lightweight, highly portable, and well-documented.

2. Kubernetes Support:

   • Create Kubernetes YAML files and Helm charts to manage the server deployment in a Kubernetes cluster.
   • Ensure support for auto-scaling based on incoming traffic load.
   • Ensure the solution is flexible enough for use with different cloud providers (AWS, GCP, Azure).

3. Metrics and Monitoring:

   • Integrate Prometheus for metrics collection, and provide basic metrics like request count, response time, error rates, resource usage and many more.
   • Set up Grafana dashboards for visualizing these metrics.
   • Expose metrics in a format that Prometheus can scrape (e.g., HTTP endpoints).

4. Optional:

   • Add CI/CD pipeline integrations to automatically build and deploy Docker images and Kubernetes configurations.

What You Need to Do

If you’re interested in tackling this issue, here’s how to get started:

1. Proposal:

   • Before diving into the code, please provide a detailed implementation plan in the comments section of this issue. Outline how you plan to implement Dockerization, Kubernetes support, and monitoring.
   • Be sure to include:
     • How you’ll structure the Dockerfile.
     • Any specific Kubernetes configurations or Helm charts you'll use.
     • How you’ll integrate Prometheus and Grafana, and what specific metrics will be exposed.

2. Read Project Files:

   • Read the README.md file carefully. This will give you a better understanding of the project’s goals and the overall architecture.
   • Review the CONTRIBUTING.md file. It contains important guidelines, particularly for newcomers. Following this will ensure that your contribution process is smoother and aligns with the project’s standards.

Additional Considerations

Here are some additional factors to keep in mind:

• Cloud-agnostic: Ensure that your Docker and Kubernetes setup is cloud-agnostic, meaning it should work on platforms like AWS, GCP, Azure, etc.
• Scalability: Design the solution to handle high traffic, with Kubernetes autoscaling based on CPU/memory usage or request load.

Next Steps

Once your implementation plan is approved, you can begin working on the feature. Make sure to:

• Regularly update the issue with your progress.
• If you hit any blockers, ask questions or request feedback from the community.

Let's make this server cloud-native and ready for the real world! 🌐