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#docker #dockerlab #dockerlabs #devops
At some point we’ve all said the words, “But it works on my machine.” It usually happens during testing or when you’re trying to get a new project set up. Sometimes it happens when you pull down changes from an updated branch.
Every machine has different underlying states depending on the operating system, other installed programs, and permissions. Getting a project to run locally could take hours or even days because of weird system issues.
The worst part is that this can also happen in production. If the server is configured differently than what you’re running locally, your changes might not work as you expect and cause problems for users. There’s a way around all of these common issues using containers.
A container is a piece of software that packages code and its dependencies so that the application can run in any computing environment. They basically create a little unit that you can put on any operating system and reliably and consistently run the application. You don’t have to worry about any of those underlying system issues creeping in later.
Although containers were already used in Linux for years, they became more popular in recent years. Most of the time when people are talking about containers, they’re referring to Docker containers. These containers are built from images that include all of the dependencies needed to run an application.
When you think of containers, virtual machines might also come to mind. They are very similar, but the big difference is that containers virtualize the operating system instead of the hardware. That’s what makes them so easy to run on all of the operating systems consistently.
Since we know how odd happenings occur when you move code from one computing environment to another, this is also a common issue with moving code to the different environments in our DevOps process. You don’t want to have to deal with system differences between staging and production. That would require more work than it should.
Once you have an artifact built, you should be able to use it in any environment from local to production. That’s the reason we use containers in DevOps. It’s also invaluable when you’re working with microservices. Docker containers used with something like Kubernetes will make it easier for you to handle larger systems with more moving pieces.
#devops #containers #containers-devops #devops-containers #devops-tools #devops-docker #docker #docker-image
Docker is an open platform that allows use package, develop, run, and ship software applications in different environments using containers.
In this course We will learn How to Write Dockerfiles, Working with the Docker Toolbox, How to Work with the Docker Machine, How to Use Docker Compose to fire up multiple containers, How to Work with Docker Kinematic, Push images to Docker Hub, Pull images from a Docker Registery, Push stacks of servers to Docker Hub.
How to install Docker on Mac.
#docker tutorial #c++ #docker container #docker #docker hub #devopstools
Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub
In this video lesson you will learn:
#docker #docker hub #docker host #docker engine #docker architecture #api
A swarm consists of multiple Docker hosts that run in swarm mode and act as managers (to manage membership and delegation) and workers (which run swarm services). A given Docker host can be a manager, a worker, or perform both roles.
When Docker is running in swarm mode, you can still run standalone containers on any of the Docker hosts participating in the swarm, as well as swarm services. A key difference between standalone containers and swarm services is that only swarm managers can manage a swarm, while standalone containers can be started on any daemon.
In this demonstration, we will see how to configure the docker swarm and how to perform basic tasks.
3. The memory should be at least 2 GB and there should be at least 2 core CPUs for each node.
#docker #containers #container-orchestration #docker-swarm
So, you want to start a radio station, eh?
This is the first part of a multi-part series on designing and building non-trivial containerized solutions. We’re making a radio station using off-the-shelf components and some home-spun software, all on top of Docker, Docker Compose, and eventually, Kubernetes.
In this part, we’re going to explore how the different parts of the system interface with one another, to set the stage for our next post, where we Dockerize everything!
I first met Icecast (https://icecast.org/) when I worked at a web-hosting startup around the turn of the millennium. One night, one of my co-workers and I had the crazy idea to load a bunch of audio files on the networked file server and stream them to our workstations. We could listen to music while we worked 90+ hours a week. Strange times. After realizing it wasn’t as simple as exporting
.ogg files over HTTP, we found Icecast (and its pal, Ices2) and built a rudimentary, local-network broadcast radio station.
#open source #cloud #tutorial #docker #containers #docker containers #docker container