Nowadays, containers are incredibly helpful for improving security, reproducibility, and scalability in both software development and data science. Inside the containers, Docker is a platform to develop, deploy, and run applications. Their upswing is one of the most important trends in technology. So, if you’re a working in software development or data science, Docker is truly in your future.
Docker Makes Life Much Easier
Docker is designed to create, organize, and run applications by using containers which allow a Tech developer to package up an application with all of the necessary parts and ship it all out as one full package. By doing this, the developer can rest and assure that the application will run on any other Linux machine no matter what customized settings that machine might have.
This way, it is likely to be a virtual machine. However, it allows applications to use the same Linux kernel that they're running on and only requires applications not already running on the host computer, which is not similar to a virtual machine. Performance will be boosted by reducing the size of the application.
Moreover, it is open source, which means that everyone can contribute and extend it to meet their own needs if any features aren't available out of the box.
For this convenience, it is designed for both developers and system administrators. They can focus on writing code without concerning about the system ultimately be running on. It also allows them to get a head start by using thousands of programs already designed to run in a Docker container. And because of its small footprint and lower overhead, it gives flexibility and potentially reduces the number of systems needed to operations staff.
As the definition of it, metaphors help us make sense of new things. To illustrate, the metaphor of a physical container helps us fast grasp the spirit of a virtual container.
Resembling a physical plastic container, a Docker container has some characteristics:
Holding things — Something is either inside the container or outside the container.
Being portable — It can be used on your local machine, your coworker’s machine, or a cloud provider’s servers (e.g. AWS). Sort of like that box of childhood knickknacks you keep moving with you from home to home.
Clear interfaces for access — Our physical container has a cover for opening and putting things in and taking things out. Likewise, a Docker container has several interfacing mechanisms with the outside world. It has ports that can be opened for interacting through the browser or with data through the command line.
Being obtained from a remote location — You can get another empty plastic container from Amazon.com when you need it. Amazon gets it from manufacturers. In the case of a Docker container, an offsite registry keeps an image for your container. Later, anytime you need a container you can make one from that image.
But unlike a virtual Docker container, a plastic container from Amazon won’t come with a copy of your goods inside and will cost you money.
Another way you can think of a Docker container is as a living instance or something that exists in particular form. It is similarly a Docker image brought to life. It’s not just code but actually bring something to life. Like other living things, the instance will eventually die, which means it will shut down.
As well as the metaphor and the living instance, you can also think of it as a software program because at its most basic level a Docker container is a set of instructions that manipulate other bits. While it is running, it generally has inside running programs. The programs in a container perform actions so your application will do something responsively.
With it you can run multiple containers at the same time on a host machine. And similar to other software programs, Docker containers can be run, stopped, and deleted.
The precursors to Docker containers are virtual machines which also isolate an application and its dependencies. However, Docker containers are superior to its precursors because they are very portable and faster to spin up as they run user space on top of an operating system's kernel instead, which makes them very lightweight.
What’s an image?
The meaning of this term in the context of it does not match a physical image.
Docker images are more like blueprints or cookie cutters. They are the immutable master template used to pump out containers that are all exactly alike.
Each image contains the Dockerfile, libraries, and code needed to run, all bundled together.
A Dockerfile is an instruction file for how Docker should build your image.
The Dockerfile refers to a base image used to build the initial image layer. Popular official base images usually include python, ubuntu, and alpine.
According to the instructions in the Dockerfile, additional layers can then be stacked on top of these base image layers. For instance, a Dockerfile for a machine learning application could tell Docker to add NumPy or Scikit-learn in an intermediate layer. Ultimately, a writable layer is stacked on top of the other layers consistent with the Dockerfile code.
If you want anyone to make containers from your image, you can send the image to a container registry. In this case, we have Docker Hub which is the largest registry and the default.
Docker and security
Docker provides security to applications running in a shared ecosystem. However, containers themselves are not an alternative to taking proper security measures.
Finally, it has a mission to make your code as portable as possible and making that portability user friendly and simple. Just like making a pizza, making an app in a Docker container take you some time, some efforts and some work, but at the end you will have something really cool and worth – trying anyway.
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