sample-microservices VS kubernetes

Skyramp provides a sample project, sample-microservices, which serves as an excellent starting point for demonstrating testing and mocking with a full-featured distributed application. The application is based on Google's Online Boutique repo, which is an e-commerce store consisting of 11 different microservices. The docker-compose-demo branch referenced above showcases how Skyramp can be seamlessly integrated with Docker Compose for testing microservices with no local setup required. You can also clone the repository and explore the structure of the microservices setup for your own purposes.

Now, let's get into the nitty-gritty of how to generate tests for your distributed applications using Skyramp. We will use our tried and true sample-microservices repo on GitHub, which is an 11-tier distributed app with REST support already configured for deployment to a Kubernetes cluster. The app itself is a complete web-based e-commerce store created by Google, known as Online Boutique.

Our starting point is the hot-code-reload-demo branch in Skyramp's letsramp/sample-microservices GitHub repo. You can use your browser to navigate to the correct branch in the repo here. The sample-microservices repo contains a demo project based on GCP's Online Boutique with added support for REST and Thrift APIs. This sample e-commerce application is perfect for demonstrating cloud-native development and testing, including debugging with Hot Code Reload with Skyramp.

This blog features the Skyramp fork of Google’s popular cloud-native microservices demo app, Online Boutique. Online Boutique is a web-based e-commerce app containing microservices that mimic real-world services, such as a product catalog, shopping cart, ad service, recommendation service, payment service, and others. The services use gRPC APIs by default, but Skyramp has also added support for REST and Thrift APIs.

We figured the community could benefit from this project. So I’m here to share it with you, and to get your invaluable feedback. We hope to keep maintaining this project with your help and insights. Please let me know what you think. All feedback will be super appreciated!

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.