Kubernetes vs. Docker

Kubernetes and Docker each play a vital role in modern, microservices-based application development. Since Kubernetes and Docker work in unison to help develop, deploy, and manage large-scale applications – they are not mutually exclusive technologies and they are certainly not in competition with each other. Nevertheless, Kubernetes and Docker are often misunderstood by the non-developer community. 

To clear up the confusion around Kubernetes vs. Docker, we’ve written this guide. After reading each section, you will understand what Kubernetes and Docker are, how they work together, and why are they essential to modern application development. 

We’re going to cover a lot of ground, so feel free to navigate this guide with the following links:

Overview of Containerization, Kubernetes, and Docker

Let’s start with a brief definition of the concepts at the core of this discussion: Containerization, Docker, and Kubernetes:

Why Use Docker Containers?

Containers are primarily used to build microservices-based applications. In contrast to a traditional monolithic application – where all of an application’s services and features are coded into the same piece of programming – a microservices-based architecture breaks a monolith into its component services and features. Then, the microservices architecture runs each of these services and features as an autonomous application of its own (called a microservice). By loosely connecting these microservices with APIs, the microservices can interact with each other to form a more flexible and “pluggable” architecture that’s easier to update and scale.

Docker containers fit into the microservices equation because they are an efficient way to run each microservice in isolation. Compared to running each microservice on its own virtual machine (VMs), for example, containers are faster to start up, use fewer system resources, and don’t require a separate OS instance. 

Here are some of the benefits of containerization for microservices-based architectures:

Why Deploy Multiple Docker Nodes on Different Servers?

Using a container orchestration tool like Kubernetes to build a system made up of multiple Docker nodes (with containerized apps running on different OS instances) offers the following advantages: 

What Is Docker? Harnessing the Power of Containers In-Depth

When talking about Docker, it’s important to determine whether you’re referring to (1) the docker container file format or (2) the open-source platform that helps developers create, run, and automate the deployment of docker files:

  1. The container file format: As a container file format, a docker image contains only the components – code, libraries, tools, and dependencies – that an application needs to run in isolation. In recent years, docker images have become the de-facto format for running containerized applications and containerized microservices. 
  2. The open-source containerization platform: In addition to being a file format, Docker is also an open-source suite of containerization tools that are accessible via the Windows/Mac dashboard called Docker Desktop. This platform features containerization tools like Docker Engine, a runtime environment that allows you to create, run, and automate the deployment of containers on different operating systems. It also includes Docker Hub, a repository that allows you to find, publish, and share Docker images. Docker Desktop also includes tools like Docker CLI (Command Line Interface) Client, Docker Compose, Notary, Credential Helper, Docker Swarm, and Kubernetes.

How Docker Works

As a containerization tool, Docker’s operation depends on a client-server architecture that consists of several fundamental components:

Here’s how these components work together:

  1. Create a Docker image: Submit a “build” command to the Docker daemon API with Docker CLI Client. 
  2. Save the image: Docker daemon creates the Docker image and saves it to Docker Registry. Docker daemon can save the image locally or remotely via Docker Hub.
  3. Pull an image from the registry (alternatively): Instead of creating a new Docker image, you can use a “pull” command, and pull an existing container image from Docker Registry or Docker Hub.
  4. Deploy the Docker container: Run the docker image by submitting a “run” command to Docker daemon with Docker CLI Client. This deploys the container.

What Is Container Orchestration?

When developing an application architecture or IT infrastructure that consists of several containers, you’ll need a container orchestration tool for managing resources across the system. The simplest of these tools is Docker Compose. Docker Compose allows you to orchestrate, manage, and schedule the deployment of containers for a multi-container system.

Docker Compose is only useful for container orchestration when all of the containers are running on a single Docker node (i.e., a single server instance). Managing and deploying containers across multiple Docker nodes that are running on different OS instances requires more sophisticated tools – like Docker Swarm or Kubernetes – for container orchestration (more on this in the next section). 

Advantages of Using Docker Containers

To sum up this section on Docker, let’s review the most compelling advantages of using Docker containers:

What Is Kubernetes? Enter Container Orchestration

When a microservices-based application (or IT infrastructure) consists of many different containers running on many different servers, managing the system is complicated. This is where the open-source container orchestration tool like Kubernetes can help. 

Kubernetes offers an API that developers use to manage and automate requests between containerized apps, deploy or replicate Docker nodes and containers when required, and give more or less processing power to container instances in response to user/client traffic levels. Kubernetes gives you a single command line or dashboard to set the rules that organize your container-based architecture. 

Another way to understand Kubernetes is to see it as a “meta-process” that automates and controls the lifecycle of the containerized applications that form an architecture. Developers code this process as a set of deployment instructions rendered in the human-readable programming language YAML. Once the process is set, the Kubernetes deployment supervises and controls incoming requests and performance across the architecture according to the instructions. 

By following these rules and limits set by developers, Kubernetes knows when to deploy, replicate, restart, and scale containers (and groups of containers) and how to load balance incoming requests and divert processing power to achieve optimum system performance across the network. 

How Kubernetes Works

When managing a container-based system with Kubernetes, the entire architecture – or the group of containers that Kubernetes orchestrates – is called a “Kubernetes cluster.” A Kubernetes cluster needs the following three components to operate:

Pods: A pod is the basic unit of deployment within the Kubernetes cluster. A pod consists of a single containerized app or a group of containerized applications that need each other to operate. For example, if a web server requires a Redis caching server, both get wrapped into the same pod – and both get spawned when that particular pod is deployed. Kubernetes’ ability to manage multi-container pods is one of its advantages as a container orchestration tool; however, if a containerized app can run on its own, it can be assigned to a single-container pod. 

Worker Nodes (Docker Nodes): A Worker node refers to a single Docker instance running on its own OS instance. A Docker node – which is sometimes called a worker node or just a node – can run a single or multiple containerized applications. 

A worker node consists of the following components:

Kubernetes master node: The Kubernetes master node is the core of the operation. This is the OS instance where you install and run Kubernetes. It hosts the Kubernetes process that schedules pods and distributes resources to pods that are running on the worker nodes that make up the architecture. Most Kubernetes clusters have more than one master node running for redundancy purposes. 

A Kubernetes master node consists of the following components:

Advantages of Using Kubernetes for Container Orchestration

Here are some of the most compelling advantages of using Kubernetes for container orchestration:

When to Use Kubernetes vs. Docker Swarm

A comparison of Kubernetes and Docker wouldn’t be complete without addressing Docker Swarm, a container orchestration tool that Docker developed for managing multi-node systems. The fact is, both Kubernetes and Docker Swarm serve distinct use-cases. Here’s when you might want to use one over the other: 

Final Thoughts on Kubernetes vs. Docker

After reading this guide, you should have a clear understanding of how Docker and Kubernetes work together to build and orchestrate a large-scale container-based application architecture. As a final review of what we’ve covered:

Docker serves as the core of any container-based architecture because Docker allows you to create and automate the deployment of multiple containerized apps on a single OS instance. 

Kubernetes allows us to manage a more complex architecture that consists of multiple Docker nodes and containers – even hundreds of thousands of containers – running on different operating system instances across a network. As a container orchestration tool, Kubernetes automates the management, scaling, updating, adding, removing, and load-balancing across a cluster of containers running on different operating system instances.

When used in conjunction, Docker and Kubernetes can tackle virtually any kind of scaling and container orchestration challenges faced by a microservices-based application or IT infrastructure. 

What’s Next After Docker and Kubernetes?

Docker and Kubernetes are fundamental aspects of modern application design – especially when you’re building and maintaining a complex, highly scalable system. However, they are just two arms of a many-armed beast. In addition to these containerization tools, you also need a strategy to monitor the performance of the individual apps and services that make up the system. 

This is where ScoutAPM can help. Scout is an application performance monitoring product that helps developers drill down into the fine-grained details of app performance and stability issues. With Scout, you can get to the bottom of the exact cause of N+1 database queries, sluggish queries, memory bloat, and other performance abnormalities. 

Want to try Scout for yourself? Contact our team to schedule a demo now