Deploying GoLang Application on Kubernetes

Abstract

This project presents a complete DevOpspipeline for a secure Golang application connected to a PostgreSQL database, fully automated, and containerized, deployed into a Kubernetes cluster, with observability, continuous integration and deployment, vulnerability-free image builds, and load testing. The application is first built in Go and containerized using KO, then run locally via Docker. A Kubernetes cluster (via ksctl) is set up, the application is deployed with HTTPS enabled via Cert?Manager and the Gateway API. The PostgreSQL database is managed with CloudNative PG to ensure high-availability and performance. Observability is implemented via Prometheus and Grafana, exposing custom metrics from the application. Continuous Integration (CI) is done via GitHub Actions, Continuous Deployment (CD) via Argo CD. A secure build process uses BuildSafe and KO to generate an image with 0 known CVEs. Finally, load-testing is performed using K6. This work demonstrates modern DevOps practices, security hygiene,observability, and high-scalability in a real-world scenario

Introduction

This project presents a DevOps-based cloud-native system that demonstrates a fully automated end-to-end software delivery pipeline for a GoLang application.

The main goal is to overcome limitations of traditional deployment methods—such as manual processes, poor scalability, downtime, and lack of monitoring—by implementing a modern DevOps architecture using containerization, Kubernetes orchestration, CI/CD automation, monitoring, and security practices.

The system is built around a GoLang backend application with PostgreSQL, containerized using Docker/KO and deployed on a Kubernetes cluster. Kubernetes manages scaling, load balancing, fault tolerance, and application lifecycle using Pods, Deployments, ReplicaSets, and Services.

A complete CI/CD pipeline (GitHub Actions/ArgoCD) automates building, testing, and deploying updates whenever code changes occur. Prometheus and Grafana provide real-time observability through metrics and dashboards, while K6 is used for performance testing. Security and reliability are enhanced using tools like Cert-Manager, Gateway API, CloudNativePG, and BuildSafe.

The literature review highlights that while DevOps and Kubernetes significantly improve deployment speed, reliability, and collaboration, challenges remain such as cultural resistance, security complexity, and operational skills gaps. Observability and DevSecOps practices are emphasized as critical for production systems.

The methodology outlines a step-by-step workflow: application development → containerization → Kubernetes setup → deployment → CI/CD automation → monitoring → scaling and user access.

Conclusion

Overall, the system achieves a scalable, reliable, secure, and fully automated DevOps pipeline, serving as a blueprint for modern cloud-native application deployment. Future improvements include service mesh integration, advanced security automation, and multi-cloud support.

References

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Copyright

Copyright © 2026 Atharva Paithankar , Sakib Shaikh, Mayur Vithore, Varsha Nanavare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.