Chef vs Docker Compose: What are the differences?
What is Chef? Build, destroy and rebuild servers on any public or private cloud. Chef enables you to manage and scale cloud infrastructure with no downtime or interruptions. Freely move applications and configurations from one cloud to another. Chef is integrated with all major cloud providers including Amazon EC2, VMWare, IBM Smartcloud, Rackspace, OpenStack, Windows Azure, HP Cloud, Google Compute Engine, Joyent Cloud and others.
What is 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.
Chef can be classified as a tool in the "Server Configuration and Automation" category, while Docker Compose is grouped under "Container Tools".
"Dynamic and idempotent server configuration" is the top reason why over 104 developers like Chef, while over 111 developers mention "Multi-container descriptor" as the leading cause for choosing Docker Compose.
Chef and Docker Compose are both open source tools. Docker Compose with 16.4K GitHub stars and 2.52K forks on GitHub appears to be more popular than Chef with 5.83K GitHub stars and 2.35K GitHub forks.
According to the StackShare community, Docker Compose has a broader approval, being mentioned in 787 company stacks & 608 developers stacks; compared to Chef, which is listed in 359 company stacks and 80 developer stacks.
What is Chef?
What is Docker Compose?
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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.
Since #ATComputing is a vendor independent Linux and open source specialist, we do not have a favorite Linux distribution. We mainly use Ubuntu , Centos Debian , Red Hat Enterprise Linux and Fedora during our daily work. These are also the distributions we see most often used in our customers environments.
For our #ci/cd training, we use an open source pipeline that is build around Visual Studio Code , Jenkins , VirtualBox , GitHub , Docker Kubernetes and Google Compute Engine.
For #ServerConfigurationAndAutomation, we have embraced and contributed to Ansible mainly because it is not only flexible and powerful, but also straightforward and easier to learn than some other (open source) solutions. On the other hand: we are not affraid of Puppet Labs and Chef either.
Currently, our most popular #programming #Language course is Python . The reason Python is so popular has to do with it's versatility, but also with its low complexity. This helps sysadmins to write scripts or simple programs to make their job less repetitive and automating things more fun. Python is also widely used to communicate with (REST) API's and for data analysis.
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.
Out custom recipes makes it simple for developers bootstrap process (using vagrant) and that same recipe is also the one that is used to prep instances
The core tech in ACS (Azure Container Services) we spin up a Kubernetes cluster and deploy our app into staging and production environments here.