DEV Community: Priyanshu Jha

Building a Real-World Kubernetes Disaster Recovery & Backup Automation System

Priyanshu Jha — Sat, 20 Dec 2025 13:53:28 +0000

Source Code:- https://github.com/Priyanshu-ai902/k8s-disaster-recovery-automation

:) I built a Kubernetes disaster recovery and backup automation system to handle real-world failure scenarios like accidental namespace deletion or configuration loss. Kubernetes is self-healing at the pod level, but it does not protect you from human mistakes, so this project focuses on backing up and restoring the actual cluster state.

The system connects directly to the Kubernetes API using Node.js, fetches live resources, and cleans the YAML by removing runtime-specific fields like UID, resourceVersion, timestamps, and status. This makes the backups portable and safe to re-apply on the same or a different cluster. Each backup is stored in a timestamped format so the cluster can be restored to a specific point in time.

To test it properly, I deployed a production-like application, verified it was running, then intentionally deleted the resources to simulate a disaster. The restore logic reads the cleaned YAML files and recreates the resources, and I validated the recovery by watching the deployments and pods come back to a running state.

To make it production-oriented, I containerized the backup logic and ran it inside the cluster using a Kubernetes CronJob. I also implemented RBAC with a dedicated ServiceAccount and least-privilege access so the automation can safely read cluster resources without excessive permissions. This project helped me understand Kubernetes internals, metadata handling, RBAC, and how real disaster recovery systems are designed beyond basic self-healing.

Go + Docker + Kubernetes: Creating a Lightweight Website Pinger

Priyanshu Jha — Fri, 28 Nov 2025 17:43:44 +0000

:) Website Pinger with Go, Docker & Kubernetes

I built a Website Pinger Service using Golang, Docker, and Kubernetes to monitor website uptime effortlessly.
🔹 How It Works
✅ Continuously pings multiple URLs provided via environment variables.
✅ Logs an error if a site is unreachable.
✅ Gracefully handles shutdown signals for a exit.

🛠 Tech Stack
🔹 Golang – Core service for making HTTP requests.
🔹 Docker – Containerized the application for easy deployment.
🔹 Kubernetes – Created a deployment to run the service efficiently.

⚡ Key Features
✔️ Automated pinging of websites at regular intervals.
✔️ Lightweight and optimized with Alpine-based Docker images.
✔️ Kubernetes-ready with a configurable deployment.
🚀 hashtag#golang hashtag#devops hashtag#docker hashtag#kubernetes hashtag#monitoring

I Built a Full Monitoring and Alerting System with Prometheus and Grafana

Priyanshu Jha — Fri, 28 Nov 2025 17:35:11 +0000

:) Building a Complete Monitoring and Logging System with Prometheus, Grafana, Loki & Alertmanager

Effective monitoring and logging are essential to ensuring the reliability and performance of any application. I recently set up a observability system to track server health, metrics, and logs, enabling early issue detection before they affect server.

🛠️ What Are These Tools?

🔹 Prometheus – An open-source toolkit that collects time-series data, such as response times, system usage, and error rates to track performance over time.

🔹 Grafana – A powerful visualization tool that connects to Prometheus, creating interactive dashboards to transform raw data into insights.

🔹 Loki – A log aggregation system that efficiently handles large amounts of logs, indexing metadata for easy searching and visualization.

🔹 Alertmanager – Works with Prometheus to manage and route alerts, ensuring you are notified of performance issues through channels like email or alerts.

🔧 What I Did:

✅ Simulated a Node.js Server: Created a Node.js server to simulate System-intensive tasks, generating metrics for Prometheus to scrape.

✅ Configured Prometheus: Set up Prometheus to monitor metrics like CPU usage, memory, response times, and error rates for tracking server health.

✅ Set Up Grafana Dashboards: Connected Grafana to Prometheus to visualize metrics in real-time and monitor key performance indicators.

✅ Integrated Loki for Logs: Used Loki to aggregate and filter logs, categorizing them by job, service, and log level.

✅ Configured Alertmanager: Set up Alertmanager to send real-time alerts for issues like high latency or server failures.

✅ Key Benefits of This Setup:

Real-time Performance Tracking with Prometheus & Grafana

Efficient Log Management with Loki

Instant Alerts with Alertmanager

Proactive Troubleshooting via visualized logs and metrics

This setup has significantly improved my ability to monitor server health, quickly troubleshoot issues, and receive timely alerts for any potential system failures.

hashtag#Monitoring hashtag#Observability hashtag#Prometheus hashtag#Grafana hashtag#Loki hashtag#Alertmanager hashtag#DevOps hashtag#Logging hashtag#Metrics hashtag#Performance

I Automated My Workflow Using Jenkins CI/CD Inside Docker

Priyanshu Jha — Fri, 28 Nov 2025 17:31:22 +0000

:) What is Jenkins CI/CD with Docker?

Jenkins is an open-source automation tool used for continuous integration and continuous deployment (CI/CD). It enables developers to automatically build, test, and deploy their applications, streamlining the software delivery pipeline. Docker allows you to containerize applications, ensuring consistent environments across different stages of development, testing, and production.

When you combine Jenkins with Docker, it provides an isolated, reproducible, and scalable CI/CD solution, making the entire automation process efficient and seamless.

What I Did with Jenkins CI/CD and Docker:

In my recent project, I set up a Jenkins CI/CD pipeline inside a Docker container to automate the entire process.

1.Running Jenkins in Docker: I ran Jenkins in a Docker container, providing an isolated environment to manage and execute CI/CD tasks efficiently.

2.Configuring Multibranch Pipeline: I configured a Multibranch Pipeline to automatically detect and create pipelines for different branches in my project. I applied filters to focus on the important branches from github, ensuring that only relevant branches are processed by the pipeline.

3.Defining Pipeline Stages: I defined the stages of the pipeline, including Checkout, Build, Test, and Deploy. These stages allowed Jenkins to automate the process of pulling the latest code, building the application, running tests, and deploying it, all without manual intervention.

4.Pipeline Execution and Validation: After committing the changes, I triggered the pipeline, which automatically executed the stages and confirmed that everything was running smoothly, from the code checkout to deployment.

Looking forward to further improving this automation workflow!

hashtag#Jenkins hashtag#CI/CD hashtag#Docker hashtag#DevOps hashtag#Automation hashtag#TechJourney hashtag#SoftwareDevelopment

I Built a GitOps Pipeline with ArgoCD for Kubernetes

Priyanshu Jha — Fri, 28 Nov 2025 17:24:34 +0000

:) ArgoCD: Simplifying Kubernetes Deployments with GitOps

What is ArgoCD and Its Use?
ArgoCD is a GitOps-based continuous delivery tool for Kubernetes. It automates deployment by syncing the desired state of applications from a Git repository to your Kubernetes cluster.

Why Is ArgoCD Important?
Automation: Automatically syncs changes from Git to Kubernetes.
Version Control: Git ensures traceable and reversible changes.
Visibility: Real-time sync status on the ArgoCD dashboard.
Collaboration: Teams collaborate easily using Git as the source of truth.
How It Integrates with GitHub Actions and Workflow
GitHub Actions automates the CI process (build, test, package), while ArgoCD handles CD (deploy and sync) to Kubernetes, ensuring a seamless workflow for continuous integration and delivery.

I created a GitOps pipeline with ArgoCD by:

Writing Kubernetes YAML files in VSCode.
Pulling an image from Docker Hub.
Pushing the code to GitHub.
Linking the GitHub repo to ArgoCD, which deployed the application and managed the sync process in the dashboard. This setup automates deployments and ensures that Kubernetes always reflects the desired state from GitHub. hashtag#ArgoCD hashtag#GitOps hashtag#Kubernetes hashtag#Docker hashtag#GitHubActions hashtag#DevOps