In DevOps, Docker plays a crucial roleplay. To get into the need of Docker in DevOps we need to know first what is Docker.
Docker is a PaS(Platform as a Service) platform that provides a way to package and run software applications in a standardized way using something called containers. These containers bundle everything your application needs to run, like code, libraries, and dependencies, so it works consistently regardless of the underlying operating system or environment.
Think of Docker as similar to virtual machines, but much more lightweight. While a virtual machine runs an entire operating system for each instance, Docker containers share the operating system of the host machine, making them faster to start, less resource-intensive, and easier to manage.
Docker helps developers and teams by providing tools to build, test, and deploy applications more efficiently. It allows you to separate your application from the infrastructure it runs on, which means you can develop and test your app in one environment and then deploy it anywhere without worrying about compatibility issues.
Using Docker, you can create and manage multiple environments for your application, automate the deployment process, and ensure that your app works the same way in development, testing, and production. This reduces delays, minimizes errors, and speeds up the process of bringing your software to production, making Docker an essential tool for modern software development.
Why we need Docker?
Imagine a team of developers is working on a web application. Each developer uses different operating systems and configurations:
Developer 1: Ubuntu with Python 3.9
Developer 2: macOS with Python 3.8
Developer 3: Windows with Python 3.7
Now, when the code is tested on their local machines, it might work for one developer but fail for another due to different Python versions, libraries, or system dependencies.
In such a scenario, your project might face compatibility issues on their systems due to differences in base images and operating systems.
To address this challenge, Docker provides an effective solution. With Docker, you can bundle your project along with its required base os or base image, operating system, libraries, and dependencies into a container. This guarantees that your project will run consistently across all systems, regardless of your teammates' configurations.
This container ensures that your app behaves the same no matter where it's running—whether on your laptop, a server, or in the cloud.
Here’s how Docker helps:
Fixes Compatibility Issues: Docker makes sure your app works the same on all systems, so you don’t need to worry about different setups.
Speeds Up Work: It makes building, testing, and deploying apps much faster.
Makes Sharing Easy: You can share and run apps without any complicated setup.
Helps with Growth: If your app needs to support more users, Docker lets you quickly add more containers to manage the extra load.
In short, Docker makes app development simpler, faster, and ensures everything runs consistently everywhere.
Docker Conatiner
Think of Docker containers as running versions of Docker images. Just like objects are created from classes in programming, containers are created from images and they are where your applications run.
Containers are usually separate from each other and from the host system, meaning they don’t mix with other containers or the machine running them.
When you delete a container, everything inside it—including data and settings—gets deleted. But if you use something called persistent volumes, you can save your data and restore it later, just like in Kubernetes.
For example, when you run:
docker run -it ubuntu
Docker will start a new container from the Ubuntu image and let you work inside it.
Every time you run a Docker command, it creates a new container unless you specify an existing container’s name or ID.
Docker Image
A Docker Image is like a template that contains instructions to create a Docker container. It’s read-only, meaning it doesn’t change once created.
Sometimes, one image is built on top of another. For example, you can create an image based on Ubuntu and add software like Apache or BusyBox to it.
To create an image, we use a Dockerfile, which has a list of commands. Each command in the Dockerfile adds a layer to the image.
If you update the Dockerfile and rebuild the image, only the changed parts are updated. This makes Docker images quick and lightweight because only the layers with changes are rebuilt.
It’s not just that; there’s much more to it. The deeper you dive into it, the more you’ll understand.
I hope you like teh concept reading :)