What is Docker?

Docker is an open platform for developing, shipping, and running applications. Docker enables you to separate your applications from your infrastructure so you can deliver software quickly. With Docker, you can manage your infrastructure in the same ways you manage your applications. By taking advantage of Docker’s methodologies for shipping, testing, and deploying code, you can significantly reduce the delay between writing code and running it in production.

When going through this Docker tutorial, we need to first understand about Docker. Docker is an OS virtualized software platform that allows IT organizations to easily create, deploy, and run applications in Docker containers, which have all the dependencies within them. The container itself is really just a very lightweight package that has all the instructions and dependencies—such as frameworks, libraries, and bins—within it.

The container itself can be moved from the environment to the environment very easily. In a DevOps life cycle, the area where Docker really shines is deployment, because when you deploy your solution, you want to be able to guarantee that the code that has been tested will actually work in the production environment. In addition to that, when you’re building and testing the code, having a container running the solution at those stages is also beneficial because you can validate your work in the same environment used for production. 

You can use Docker in multiple stages of your DevOps cycle, but it is especially valuable in the deployment stage. Next up in this Docker tutorial is the advantages of Docker.

Now as you know what is Docker, you must know the difference between Docker and virtual machines. So, let’s begin.

you’ll notice some major differences, including:

The virtual environment has a hypervisor layer, whereas Docker has a Docker engine layer. There are additional layers of libraries within the virtual machine, each of which compounds and creates very significant differences between a Docker environment and a virtual machine environment.
With a virtual machine, the memory usage is very high, whereas, in a Docker environment, memory usage is very low. 
In terms of performance, when you start building out a virtual machine, particularly when you have more than one virtual machine on a server, the performance becomes poorer. With Docker, the performance is always high because of the single Docker engine. 
In terms of portability, virtual machines just are not ideal. They’re still dependent on the host operating system, and a lot of problems can happen when you use virtual machines for portability. In contrast, Docker was designed for portability. You can actually build solutions in a Docker container, and the solution is guaranteed to work as you have built it no matter where it’s hosted. 
The boot-up time for a virtual machine is fairly slow in comparison to the boot-up time for a Docker environment, in which boot-up is almost instantaneous. 
One of the other challenges of using a virtual machine is that if you have unused memory within the environment, you cannot reallocate it. If you set up an environment that has 9 gigabytes of memory, and 6 of those gigabytes are free, you cannot do anything with that unused memory. With Docker, if you have free memory, you can reallocate and reuse it across other containers used within the Docker environment.
Running multiples of them in a single environment can lead to instability and performance issues. Docker, on the other hand, is designed to run multiple containers in the same environment—it actually gets better with more containers run in that hosted single Docker engine. 
Virtual machines have portability issues; the software can work on one machine, but if you move that virtual machine to another machine, suddenly some of the software won’t work, because some dependencies will not be inherited correctly. Docker is designed to be able to run across multiple environments and to be deployed easily across systems. 
The boot-up time for a virtual machine is about a few minutes, in contrast to the milliseconds it takes for a Docker environment to boot up.