AWS EKS deploy - Live Demo
Deploying an app to Kubernetes on AWS, with AI as my pair engineer Before AI, you'd settle on one infrastructure design up front and then build it. Today, AI lets me compare several approaches quickly and pick the most reliable one for the job. The design phase moves much faster. It's still iterative, though: the infrastructure gets tuned to the real use case one step at a time. To try this, I…
Deploying a Spring Boot application to Amazon EKS (Kubernetes on AWS) was the focus, not the application itself. The aim was to demonstrate a reliable process for getting code into production. Claude served as an AI pair engineer during the project. The workflow involved building and testing the app locally in Docker, pushing the image to Amazon ECR, creating a Kubernetes cluster, deploying the app behind a load balancer, and releasing new versions with zero downtime.
The process underwent multiple iterations, refining the scripts and documentation to ensure smooth subsequent runs. An initial challenge included AWS rejecting a server type on a new account's free plan, requiring adjustments to the Docker image and subsequent cleanup. Additionally, version tags needed correction to prevent the cluster from searching for non-existent images.
These issues were incorporated back into the scripts and documentation, ensuring future runs function seamlessly. The outcome is a basic script for each step, which can be run in sequence, replicated by anyone following the provided README. The next goal is to automate the entire process using a CI/CD pipeline, allowing each code push to build, test, and deploy the new version independently. The code and a step-by-step guide can be found at https://lnkd.in/g_GNvWqf.
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