However, at the moment Docker is the biggest and the most powerful player on the market. Keep in mind that Docker is not the first and not the only containerization platform. The diagram below shows how this looks with a Docker example. Another significant drawback of VM is a slow boot as, in addition, it runs identical processes that use CPU resources.ĭocker eliminates all the above by simply sharing the OS kernel across all the containers running as separate processes of the host OS. And by the way, at the moment, most cloud-based server providers will charge you for that extra space.
Most of the time your server will host several VMs that will take up even more space. The main problem with VM is that an “extra OS” on top of the host operating system adds gigabytes of space to the project. Traditionally, virtual machines were used to avoid this unexpected behavior. How does this differ from virtualization? This challenge can be addressed by isolating the app to make it independent of the system. You may end up in a situation when the application is working on your computer, but won’t even start on the staging server, or the dev or QA’s machine.
When you develop an application, you need to provide your code along with all possible dependencies like libraries, the web server, databases, etc. Let’s go back to software development in Python using Docker containers. Using Docker with Python is one of the most popular option in software development. Cargo of various sizes can be put inside a standardized container and loaded/unloaded by the same vehicle.
Those examples assume that you are familiar with the basic concepts of those technologies.
Along with Python, we are going to run Nginx and Redis containers. By the end of this article, you will know how to use Docker on your local machine. This is an introductory Python Docker tutorial.
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