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  • Yeah, it also has the effect that when starting up say a new postgres or web server is one simple command, a few seconds and a few mb of disk and ram, you do it more for smaller stuff.

    Instead of setting up one nginx for multiple sites you run one nginx per site and have the settings for that as part of the site repository. Or when a service needs a DB, just start a new one just for that. And if that file analyzer ran in it's own image instead of being part of the web service, you could scale that separately.. oh, and it needs a redis instance and a rabbitmq server, that's two more containers, that serves just that web service. And so on..

    Things that were a huge hassle before, like separate mini vm's for each sub-service, and unique sub-services for each service doesn't just become practical but easy. You can define all the services and their relations in one file and docker will recreate the whole stack with all services with one command.

    And then it also gets super easy to start more than one of them, for example for testing or if you have a different client. .. which is how you easily reach a hundred instances running.

    So instead of a service you have a service blueprint, which can be used in service stack blueprints, which allows you to set up complex systems relatively easily. With a granularity that would traditionally be insanity for anything other than huge, serious big-company deployments.

  • Nine. How much ram do they use? How much disk space? Try running 90, or 900. Currently, on my personal hobby kubernetes cluster, there's 83 different instances running. Because of the low overhead, I can run even small tools in their own container, completely separate from the rest. If I run say.. a postgresql server.. spinning one up takes 90mb disk space for the image, and about 15 mb ram.

    I worked at a company that did - among other things - hosting, and was using VM's for easier management and separation between customers. I wasn't directly involved in that part day to day, but was friend with the main guy there. It was tough to manage. He was experimenting with automatic creating and setting up new VM's, stripping them for unused services and files, and having different sub-scripts for different services. This was way before docker, but already then admins were looking in that direction.

    So aschually, docker is kinda made for people who runs things in VM's, because that is exactly what they were looking for and duct taping things together for before docker came along.

  • VM's have much bigger overhead, for one. And VM's are less reproducible too. If you had to set up a VM again, do you have all the steps written down? Every single step? Including that small "oh right" thing you always forget? A Dockerfile is basically just a list of those steps, written in a way a computer can follow. And every time you build an image in docker, it just plays that list and gives you the resulting file system ready to run.

    It's incredibly practical in some cases, let's say you want to try a different library or upgrade a component to a newer version. With VM's you could do it live, but you risk not being able to go back. You could make a copy or make a checkpoint, but that's rather resource intensive. With docker you just change the Dockerfile slightly and build a new image.

    The resulting image is also immutable, which means that if you restart the docker container, it's like reverting to first VM checkpoint after finished install, throwing out any cruft that have gathered. You can exempt specific file and folders from this, if needed. So every cruft and change that have happened gets thrown out except the data folder(s) for the program.

  • Or they call tech support and say their computer doesn't work anymore

  • laughs in Kubernetes