Back in 2013, Docker began to gain popularity by allowing developers to quickly create, execute, and scale their applications by creating containers. Part of its success is due to being Open Source and the support of companies such as IBM, Microsoft, RedHat, or Google. Docker, in just two years, was able to turn a niche technology into a fundamental tool available to everyone thanks to its greater ease of use. In this article, complete Kubernetes vs Docker features are evaluated.
Its evolution has been unstoppable, currently representing one of the common mechanisms to deploy software on any server through software containers.
Google, Microsoft, Amazon, Oracle, WMware, IBM, and RedHat are betting heavily on these technologies, offering all kinds of services to cloud developers.
Today everything is aimed at being dockerized, as popularly in the USA refers to the fact of packaging a software application to be distributed and executed through the use of these software containers.
Most of you may not be familiar with the term. But if you are a software developer, you should start learning more about it since it is the real revolution in years of the software industry.
Thanks to Kubernetes vs Docker, the developers able to become independent of the sysadmin to some extent. Embraced the concept of DevOps more openly. That is, we can create code and distribute it efficiently without headaches.
What are the software containers?
To explain what software containers are, let’s go down to the simplest level of abstraction
Looking for some analogy with the real world, we can talk about those containers that we can see being transported by boat from one place to another.
We do not care about its content but its modular form to be stored and transported from one place to another as boxes.
Something similar happens with software containers. Within them, we can host all the dependencies that our application needs to get executed. Starting with the code itself, the libraries of the system, the execution environment, or any type of configuration.
We don’t need much more from outside the container. Inside they are isolated to be executed anywhere.
Containers are the solution to the usual problem. For example, of moving between development environments such as a local machine or in a real production environment.
We can safely test an application without worrying that our code behaves differently. This is because everything we need is inside that container.
Everything you need is inside the container itself and is invariable.
In short, containers represent a logical packaging mechanism where applications have everything they need to run — describing it in a small configuration file.
With the advantage of being able to be;
- Easily replicated by other developers or by system administrators; you have to scale those applications without knowing internally how our app works
The Docker file will suffice to adapt the execution environment and configure the server where it will be mounted. From that file, you can generate an image to display on a server in seconds.
Kubernetes vs Docker Containers against Virtualization
One of the main doubts is how a software container and a virtual machine differ then. This concept is much earlier than that of the containers themselves.
Thanks to virtualization, we are able, using the same computer, to have different virtual machines with their guest operating system, Linux or Windows. All this is running on a host operating system and with virtualized access to the hardware.
Virtualization is a common practice on servers to host different applications or in our work environment to run different operating systems.
Many traditional hosting accommodations based on creating limited virtual machines on the same server. Which host our web servers in isolation, and shared by a dozen clients.
In contrast to virtual machines, containers run on the same host operating system in isolation without their operating system.
Since they share the same Kernel, which makes them much lighter, where we get three virtual machines, we can probably multiply it by a large number of software containers.
A Kubernetes vs Docker container can occupy only a few tens of megabytes while a virtual machine, having to emulate an entire operating system, can occupy several gigabytes of memory.
Which ultimately represents the first point in cost savings.
Usually, each application in Docker goes in its own fully insulated container. While in VMs it is generally due to the dimensioning to have several applications on the same machine with its dependencies, much worse to scale horizontally.
Containers are based on two mechanisms to isolate processes in the same operating system.
The first of these is the namespace provided by Linux. Allowing each process only to be able to see its own “virtual” policy (files, processes, network interfaces, hostname or whatever).
The second concept is CGroups, by which we can limit the resources it can consume (CPU, memory, bandwidth, etc.)
From monolithic applications to microservices
Before we start talking about Kubernetes as another of the important actors on how the way of developing and scaling applications has changed, we will analyze the evolution of these architectures in recent years.
The classic definition of a monolithic application refers to a set of fully coupled components developed, deployed, and managed as a single entity.
Practically, they are encased in the same process very difficult to scale, only vertically adding more CPU, memory.
As a programmer, you need to have all that code and run the tests by raising a single instance with everything. Even if the change you want to make is minimal.
Not to mention how expensive it becomes every time you want to create a new release in;
In contrast to this, the concept of microservices emerged that allows several small applications to communicate with each other to offer specific functionality.
For example in Kubernetes vs Docker;
We have the case of Netflix, one of the companies that began to use microservices intensively. Although we do not have a specific figure, we can estimate according to the data. Many of their technical talks that have more than 700 microservices.
We can talk about a container with a microservice that is responsible for serving the video according to the platform from which we access, whether mobile, smart tv or tablet.
We could also have another one that is in charge of the content history — another one for the recommendations. Finally, another one for the payment of the subscription.
All of them can live in the Netflix microservices cloud and communicate with each other. We do not need to modify them at all. Since we can scale some of the containers that have any kind of microservices and be practical on the fly.
After this, we can see in a more precise way how all these microservices have taken the form of dockerized containers communicating with each other through the system.
Kubernetes: the need to have an orchestra teacher
If the number of applications grows in our system, it becomes complicated to manage.
Docker is not enough solely since we need;
- Coordination to make the deployment
- The supervision of services
- The replacement
- The automatic scaling
- Administration of the different services that make up our distributed architecture
Google is probably the first company that realized that it needs a better way to implement and manage its software components to scale globally.
For years, Google internally developed Borg (later called Omega).
In 2014, after almost a decade of intensive internal use, Kubernetes presented as an Open Source system based on learning using large-scale services.
It was at the 2014 DockerCon when Eric Brewer, VP of Engineering, jokingly presented him as being another orchestration platform.
A dozen similar systems offered at the DockerCon 2014, some public and some internal, such as Facebook or Spotify.
The most important thing is that Kubernetes is, it is designed to use anywhere. So it can orchestrate deployments in sites, in public clouds, and hybrid deployments.
The future of Kubernetes vs Docker containers
Container use adoption will continue to grow. We also see some standardization around Kubernetes vs Docker. This will drive the growth of a large number of related development tools.
The technological stack begins to mature quite a lot. Almost all the suppliers start to be compatible with each other, thanks to Docker and Kubernetes.
Google, Microsoft, Amazon, or IBM, for example, already are and work under the same standard. The fight is now to move all that workload that is not yet in the cloud: the hybrid cloud.
There are pending challenges such as continuing to simplify the learning curve, although it has already improved in the last five years.
Despite this, developers still need to learn how to produce a Docker image. How to implement it in an orchestration system, how to configure it, and more security details.
Something nothing trivial at first. We are sure that in a short time, we will see how that is further simplified. As developers will work on higher levels of abstraction, thanks to the growing ecosystem around Kubernetes vs Docker.