Kubernetes vs. Docker - What’s the difference?

Docker is a platform for creating, packaging, and running applications in containers, while Kubernetes is an orchestration system that manages and scales those containers. In other words, Docker handles the containerization of applications, and Kubernetes takes care of automatically deploying, organizing, and scaling them.

What are the differences? Kubernetes vs. Docker

Docker has achieved a small revolution with the development of container technology. For work in software development, virtualization with self-contained packages (the containers) offers completely new possibilities. Developers can easily bundle applications and their dependencies in containers, ensuring that virtualization can occur at the process level. Although there are a number of Docker alternatives, the open-source solution Docker remains the most popular platform for creating containers.

Kubernetes, on the other hand, is an application for orchestration (that is, management) of containers; the program itself does not create the containers. The orchestration software accesses the existing container tools and integrates them into its own workflow. Thus, containers created with Docker or another tool are easily integrated into Kubernetes. Then, you use orchestration to manage, scale, and move the containers. Kubernetes ensures everything runs as desired and also provides replacements if a node fails.

Use cases for Docker and Kubernetes

In the comparison of Docker vs. Kubernetes, it is noticeable that the two tools differ in their use cases but work hand in hand. To understand the different functions of Docker and Kubernetes, let’s look at an example.

Most applications today are organized with microservice architectures because this architectural style allows for better scalability, flexibility, and maintainability by breaking down complex systems into smaller, independent services.

Step 1: Program microservices and create containers

In the first step, the application must be programmed; the team develops the individual microservices that make up the app. Each microservice is written as a standalone unit and has a defined API for communication with other services. Once the development of a microservice is completed, it is containerized with Docker. Docker allows microservices to be packaged into small, isolated containers that contain all necessary dependencies and configurations. These containers can then be run in any environment without complications arising from different system configurations.

Step 2: Configure orchestration with Kubernetes

After the microservices have been successfully containerized, Kubernetes comes into play. In the next step, the team creates Kubernetes configuration files that specify how the containers (in Kubernetes lingo, these are also called Pods) should be deployed across different servers. The files include details such as how many instances of a particular pod should be run, what network settings are required, and how communication between the microservices works.

Kubernetes takes care of the automatic management of these containers. If a microservice fails or a container crashes, Kubernetes ensures that the container is automatically restarted, allowing the application to continue functioning without system outages. Additionally, Kubernetes can perform the function of a load balancer and distribute containers across multiple servers to ensure better utilization and scalability. If traffic for the application increases, Kubernetes can automatically start new pods.

Step 3: Updates

With Kubernetes, not only is the deployment of containers simplified, but also the management of updates. If the developers want to bring new code into production, Kubernetes can gradually replace the containers with the new version without causing downtime. This ensures the application remains constantly available while new features or bug fixes are implemented.

Direct comparison of Kubernetes vs. Docker

Docker Swarm, the Kubernetes alternative

Even though Kubernetes and Docker work wonderfully together, there is still a competitor for the orchestration tool: Docker Swarm combined with Docker Compose. While Docker can handle both solutions and even switch between the two, Docker Swarm and Kubernetes cannot be combined. Therefore, users often face the question of whether to rely on the very popular Kubernetes or to use Swarm, which is part of Docker.

The structure of the two tools is essentially very similar – only the names of the individual aspects change. The purpose is also identical, which is to manage containers efficiently and ensure the most economical use of resources through intelligent scaling.

Swarm reveals advantages in installation: Since the tool is an integral part of Docker, the transition is very easy. While you have to set up orchestration with Kubernetes first—which admittedly isn’t very complex—everything is already there with Swarm. Since you are most likely already working with Docker in practice, you don’t need to familiarize yourself with the specifics of a new program.

Kubernetes shines with its own GUI: The accompanying dashboard provides not only an excellent overview of all aspects of the project but also enables the completion of numerous tasks. Docker Swarm, on the other hand, offers such convenience only through additional programs. Kubernetes also stands out in terms of functionality: unlike Swarm, which needs extra resources for monitoring and logging, Kubernetes includes these capabilities by default as part of its core features.

The main benefit of both programs lies in scaling and ensuring availability. It is said that Docker Swarm is generally better in terms of scalability. This is due to the complexity of Kubernetes, which leads to a certain sluggishness. However, the complex system ensures that automatic scaling with Kubernetes is better. Additionally, a significant advantage of Kubernetes is that it continuously monitors the state of the containers and directly compensates for any failures.

Swarm has an edge in load balancing as it provides even distribution right out of the box. With Kubernetes, achieving load balancing requires an extra step—deployments need to be converted into services before traffic can be evenly distributed.