In this beginner-friendly blog, we’ll be going through the GCP VM Disk Loss experiment for LitmusChaos. GCP VM Disk Loss experiment causes detachment of a non-boot persistent storage disk from a GCP VM instance for a specified duration of time and later re-attaches the disk to its respective VM instance. The broad objective of this experiment is to extend the principles of cloud-native chaos engineering to non-Kubernetes targets while ensuring resiliency for all kinds of targets, be it Kubernetes or non-Kubernetes ones, as a part of a single chaos workflow for the entirety of a business.

At the time of…


This blog is a beginner-friendly guide for the GCP VM Instance Stop chaos experiment for LitmusChaos. The experiment causes the shutdown of one or more GCP VM instances for a specified duration of time and later restarts them. The broad objective of this experiment is to extend the principles of cloud-native chaos engineering to non-Kubernetes targets while ensuring resiliency for all kinds of targets, be it Kubernetes or non-Kubernetes ones, as a part of a single chaos workflow for the entirety of a business.

At the time of writing this blog, the experiment is available only as a technical preview…


This is a quick tutorial on how to get started with Litmus 2.0 in Google Kubernetes Engine. We’ll first set up our basic GKE cluster throughout this blog, then install Litmus 2.0 in the cluster, and finally, execute a simple chaos workflow using Litmus.

But before we kick off the demonstration, let’s have a brief introduction to Litmus. Litmus is a toolset to perform cloud-native Chaos Engineering. It provides tools to orchestrate chaos on Kubernetes to help developers and SREs find weaknesses in their application deployments. Litmus can be used to run chaos experiments initially in the staging environment and…


Part 2: Chaos Engineering 101, Using LitmusChaos 2.0 for a custom workflow chaos experiment

This blog is part two of a two blog series that details how to get started with containerization using Docker and Kubernetes for deployment, and later how to perform chaos engineering using LitmusChaos 2.0. Find Part-1 of the blog here.

Say you have deployed your E-Commerce application using Kubernetes and you’re very satisfied with how flexible and stable your application deployment has come to be. During the testing of the application, it had checked all the boxes and you’re very confident that your application deployment is all set to face the peak hours of the sale next week, which will…


Part-1: Containers 101, Deploy a Node.js App Using Docker and Kubernetes

This blog is part one of a two blog series that details how to get started with containerization using Docker and Kubernetes for deployment, and later how to perform chaos engineering using LitmusChaos 2.0. Find Part-2 of the blog here.

So you’ve just come across this term called Containerisation and now you’re wondering that as an aspiring software product engineer, or a DevOps engineer, what role will it play in your day-to-day work. After all, applications can be deployed without containers, and in fact, that has been the norm for a long time until containerization technologies like Docker, Kubernetes came…


Photo by Mike van Schoonderwalt from Pexels

Let’s face it, even before we were properly exposed to data science we had probably heard both of these terms: overfitting and underfitting. The reason these two terms shall be regarded as the guiding philosophy of machine learning is that every machine learning model in existence conforms to the trade-off between both of these, which in turn dictates their performance and therefore every machine learning algorithm seeks to create models that offer the best trade-off between them.

But why do we care about it?

Whenever we model any data using machine learning, the end objective is that the trained model should be able to correctly predict the…


Photo by Donald Giannatti on Unsplash

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” -Josh Wills, Director of Data Engineering at Slack

We stand in midst of a deluge of data today. Starting from the smartphone in your palm to the smart refrigerator at your home, it’s everywhere. Today, over 2.5 quintillion bytes of data is generated every day, which is expected to rise up to 463 exabytes by 2025. Even though the systems that generate these vast volumes of data expire in a matter of time, the data doesn’t. …

Neelanjan Manna

SE-1 @ ChaosNative | Full-Stack Development, Data Science, ML, Cloud | In permanent beta; learning, improving, and evolving through experience and passion

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