Docker Compose vs Docker Swarm Visualizer: What are the differences?
Docker Compose: Define and run multi-container applications with Docker. With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running; Docker Swarm Visualizer: A visualizer for Docker Swarm using the Docker Remote API, Node.JS, and D3. Each node in the swarm will show all tasks running on it. When a service goes down it'll be removed. When a node goes down it won't, instead the circle at the top will turn red to indicate it went down. Tasks will be removed.
Docker Compose and Docker Swarm Visualizer belong to "Container Tools" category of the tech stack.
Docker Compose and Docker Swarm Visualizer are both open source tools. It seems that Docker Compose with 16.6K GitHub stars and 2.56K forks on GitHub has more adoption than Docker Swarm Visualizer with 2.58K GitHub stars and 406 GitHub forks.
What is Docker Compose?
What is Docker Swarm Visualizer?
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Heroku was a decent choice to start a business, but at some point our platform was too big, too complex & too heterogenic, so Heroku started to be a constraint, not a benefit. First, we've started containerizing our apps with Docker to eliminate "works in my machine" syndrome & uniformize the environment setup. The first orchestration was composed with Docker Compose , but at some point it made sense to move it to Kubernetes. Fortunately, we've made a very good technical decision when starting our work with containers - all the container configuration & provisions HAD (since the beginning) to be done in code (Infrastructure as Code) - we've used Terraform & Ansible for that (correspondingly). This general trend of containerisation was accompanied by another, parallel & equally big project: migrating environments from Heroku to AWS: using Amazon EC2 , Amazon EKS, Amazon S3 & Amazon RDS.
Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.
We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.
Since our production deployment makes use of the Convox platform, we use this to describe the containers to be deployed via Convox to AWS ECS.
We also use this for our local dev environment (previously used vagrant with chef).
Aside from our Minecraft-infrastructure, we compose it with ... Docker Compose! (kinda obious, eh .. ?) This includes for example the web-services, aswell as the monitoring and mail-infrastructure.
Docker Compose is just another part of my "infrastructure as code" initiative and allows me to build isolated pieces of systems with their own volumes and networks.
Our application will consist of several containers each communicating with each other. Using docker-compose, we can orchestrate several containers at once.