Since an image consists of layers that are shared in different images, these layers remain in different emergency situations. Since we cannot use them separately, it is safe to delete them with the docker image prune command .

To save the results of the container's work, you can mount the host machine folder to the container folder. We can explicitly specify the folder on the host machine, for example, docker run -v / page_host: / page_container nama_image , or enable it to be generated by docker run -v / page_container nama_image . To remove generated folders (volumes) that are no longer used by containers, enter the Docker volume prune command . For the collection of unused networks, there is also a garbage collector.

There is also a single garbage collector, in fact, simply combining specialized docker system prune parameters into one with logically compatible parameters . There is a tendency to put it in crowns. You can also look at the space occupied by all containers, all images and all volumes using the docker system df command , and also without grouping – docker system df -v .

Many of the issues described here by garbage collection are handled by Docker-compose. In addition, it greatly simplifies life, unless you run the container once for experiments. So the command Docker-compose up starts the containers, and docker-compose down -v removes them, and all dependencies between them are also removed. All container launch parameters are described in Docker-compose.YML, as well as the relationships between them. Thanks to this, when changing the launch parameters of containers, you do not need to worry about deleting the old ones and creating new ones, you do not need to register all the parameters of the containers – just fill in with the up parameter , and it will either re-create or update the container configuration.

To prevent cluttering the system, Docker has a built-in configurable limit on the number of containers and images, reminding you to clean the system by running the garbage collector.

Saving time on container creation

We already met in the previous topic about images, about their layers and caching. Let's look at them in terms of container creation time. Why is this so important, after all, by analogy with virtualization, the system administrator started the creation of the container and while he passes it to the programmer, by this time he will definitely be assembled. It is important to note that a lot has changed since then, namely, the principles and requirements for the ecosystem and its use have changed. So, for example, if earlier the developer, having developed and tested his code at his workplace, sent it to the QA manager for testing it for compliance with business requirements, and when his turn comes to this code, the tester at his workplace will run this code and check … Now the infrastructure is handled by DevOps, which establishes a continuous process for delivering features developed by programmers, and containers are created automatically with each submission to the production branch for automated testing. At the same time, so that the work of some tests does not affect the work of others, a separate container is created for each test, and often the tests run in parallel in order to instantly show the result to the developer, while he remembers what he did and did not switch his attention to another task.

For standard programs: no need to install, no need to maintain

We often use a huge number of ready-made solutions. When choosing a solution, we are faced with a dilemma: on the one hand, it is more universal and more proven than we can afford to do, on the other hand, it is complex enough to figure out how to properly install and configure it ourselves, in order to install all dependencies, resolve conflicts, set up for initial use. Now installation and configuration has become much easier, standardized, low-level problems are largely absent. But before we continue, let's digress and take a look at the process from getting started to starting to use the app within the story: