by Jeremy H • October 21, 2020
The rise of the microservices and container-based systems has allowed global enterprises – like Amazon.com, Netflix, Uber, and Airbnb – to achieve unprecedented market dominance. Central to making these microservices-based applications possible is the concept of containerization, and at the core of containerization are Docker and Kubernetes – the two most widespread solutions for building and managing container-based systems.
However, as much as people talk about Docker and Kubernetes, these platforms are often confused. That’s why we wrote this guide: to give you a clear understanding of what Docker and Kubernetes are all about. We’ll start by defining the concepts of containerization and container-based systems. Then we’ll teach you about Docker and Kubernetes, how they work, and how they fit into the containerization puzzle.
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Containerization is a server virtualization strategy that allows you to launch and run multiple applications or microservices within “containers” on the same operating system instance. As an isolated runtime environment, a container lets you “contain” an application as if it had its very own operating system. From the containerized application’s perspective, it is alone – unaware of any other applications running on the server – even when it is sharing server resources with multiple apps or microservices.
Compared to the traditional server virtualization method (virtual machines), containers are lighter weight and you can more economically host more of them on the same server. This is because a virtual machine needs to contain and run a replica of the entire OS instance, but a container only holds the bare minimum of code, libraries, tools, and dependencies that a microservice requires for its operation.
In recent years, service-based application architectures consisting of many containerized microservices have become increasingly popular because they offer distinct advantages over traditional monolithic applications. For example, monolithic applications intertwine all of the programming for the application within the same codebase. This makes monoliths complicated to change or upgrade without coding conflicts that can negatively impact other parts of the system. In contrast, container-based systems (consisting of multiple microservices running inside containers) have a modular, pluggable architecture that is faster to upgrade, more efficient to operate, and easier to scale than monolithic applications.
In a microservices-based app, developers break the monolith into its individual features and services – then they run each service or feature as an independent application, i.e., a microservice. By hosting each microservice in its own container, developers can loosely connect them via APIs to form a more flexible, component-based application architecture. The “pluggability” of this kind of system makes scaling and updating easier, faster, and more affordable.
Lastly, compared to using virtual machines as runtime environments for microservices, containers use fewer system resources and offer faster performance (more on this in the next section).
Here are the benefits that containers bring to the table:
Docker is a free, open-source platform for building containerized apps, deploying containerized apps, and allocating resources across a container-based architecture. By allowing you to create, deploy, and orchestrate a multi-container system, Docker helps you realize all of the container benefits we referenced above.
Before we describe Docker further, it’s important to note that the word “Docker” refers to two separate concepts: (1) The docker container file format (also known as a docker image), which holds all of the components, code, tools, libraries, and dependencies that a containerized application needs to run. (2) The free, open-source Docker platform, which includes the tools you need for creating, deploying, and managing containers and container-based systems.
Now let’s get familiar with the Docker platform, its tools, and components.
Getting familiar with the Docker platform starts with Docker Desktop. Docker Desktop runs on Windows or Mac and features a dashboard where you can access different Docker tools for creating, managing, and automating the deployment of containers. From Docker Desktop, you can access the rest of the tools in this section.
Here’s an image of the Docker Desktop interface:
The main tools included in the Docker platform are:
Now that you understand the Docker toolset, let’s review Docker’s fundamental components that you’ll be interacting with:
If you want to start creating and running docker images, watch this video for an awesome introduction to the basics!
When your microservices-based application includes multiple containerized microservices, the application will require a container orchestration tool that automates the distribution of resources, container deployment, and the orchestration of requests across the architecture. Docker Compose allows you to automate container deployment and resource distribution for a container-based system when all containers are operating under the same Docker node and operating system.
When container-based systems include multiple Docker nodes (and containers running on different server instances), you will need a more advanced container orchestration solution such as Docker Swarm or Kubernetes.
Here are some excellent user reviews for Docker from Capterra:
Like the conductor of an orchestra, Kubernetes can direct actions of thousands (even hundreds of thousands) of containers and groups of containers, all running on different servers under different Docker nodes. With Kubernetes, you can manage a group of containerized apps to work in concert – in a way that forms a larger container-based system.
As the most widely-used solution for orchestrating container-based systems, Kubernetes is not only free and open-source, it’s also being used by the U.S. Department of Defense in F-16s and Battleships.
Perhaps the easiest way to understand Kubernetes is to view it as a series of deployment instructions coded in YAML (a human-readable computer language). Developers use a single interface (be it command-line or dashboard) to interact with Kubernetes API and define these deployment instructions. After coding the instructions, Kubernetes has all of the rules and limits it needs to manage the lifecycle of the containerized microservices that comprise your system. Kubernetes will follow these rules/limits while doing the following:
Ultimately, Kubernetes carries out all of these actions on autopilot as dictated by its YAML-coded deployment instructions.
Container orchestration through Kubernetes brings the following advantages:
“Kubernetes clusters” are the container-based systems that you can create with Kubernetes. In other words, a Kubernetes cluster is the group of containerized microservices that a Kubernetes instance controls. These containerized processes will be running on multiple servers under multiple Docker nodes. Essentially, it represents your entire container-based system or application. A Kubernetes cluster consists of three primary units of deployment:
Kubernetes Pods: Pods are the smallest element of a Kubernetes cluster. A pod can be one or more containerized microservices that depend on one another for their operation. In other words, you can’t spin up one microservice within a pod without spinning up the others. Let’s say a containerized web service needs a containerized caching server to function. Both containers will belong to the same Kubernetes pod, and they will deploy/replicate in unison as required. The capacity of Kubernetes to orchestrate multi-container pods like this is a distinct advantage.
Kubernetes Worker Nodes: A single Docker node – which is running on its own server and managing the different containerized services on that server – is referred to as a Worker Node in the Kubernetes cluster. A Worker Node could be managing one or multiple containerized services. Three fundamental parts comprise a Worker Node: (1) the kubelet, (2) the kube-proxy, and (3) the Docker engine:
Kubernetes Master Node: The Kubernetes Master Node is the brain that runs the entire Kubernetes cluster. It runs on an OS instance of its own, but for redundancy, replicas of the Master Node will usually exist on multiple OS instances. As the brain of the Kubernetes cluster, the Master Node automates the process of scheduling pod deployments and allocating resources to Worker Nodes across the network as required. Four fundamental parts comprise each Master Node: (1) the kube-apiserver, (2) the kube-control-manager, (3) the kube-scheduler, and (4) the etcd.:
Here’s an image of the Kubernetes WebUI Dashboard interface:
Docker Swarm is another container-orchestration option that Docker Desktop offers alongside Kubernetes in its toolset. Since it was developed by Docker, you might think that Swarm competes with Kubernetes for the Container Orchestration use-case. However, these solutions are not directly competing with each other.
Like Kubernetes, Docker Swarm can orchestrate a large-scale system consisting of thousands of microservices. However, Docker has less of a learning curve, and setup takes a lot less time. Therefore, Swarm is great when you need to develop a container-based system as quickly as possible, and when your team doesn’t have advanced Kubernetes engineering experience. That being said, the Swarm API does not have as many features and capabilities as Kubernetes, and there’s no native monitoring.
Kubernetes includes a lot of features that Swarm doesn’t have – such as more automatic scaling capabilities when developing a large, nuanced, highly-available system. It also has a range of native monitoring features (Docker Swarm does not have native monitoring tools). That being said, due to the steep learning curve of Kubernetes and the time it takes to set up the system, it’s more appropriate for teams that already have Kubernetes engineering experience – and for container orchestration jobs that require more sophisticated configurations.
Here are some excellent user reviews for Kubernetes from TrustRadius:
In summary, here are the key takeaways to from this guide (a.k.a, the TL;DR version):
Containerization is a strategy that creates a virtual runtime environment without needing to replicate an OS instance. This provides a lighter-weight, more efficient, and higher-performance method for running multiple independent microservices on the same server. Containerized microservices allow you to build modular, container-based systems and/or microservices-based application architectures. Compared to traditional monolithic architectures, container-based systems consisting of independently running microservices are faster, easier, and more cost-efficient to operate, manage, upgrade, change, and scale.
Docker is an open-source suite of tools that allow you to create, deploy, and manage containers, containerized apps/microservices, and container-based systems. In terms of container orchestration, Docker is limited to only managing containers running on a single operating system kernel.
Kubernetes is an open-source container orchestration platform that allows you to manage complex and large-scale container-based systems made up of thousands (even hundreds of thousands) of containers hosted on different OS kernels. Kubernetes allows developers to automate deployment, scaling, replication, load-balancing, and available resources across a massive network of containers (and groups of containers) running on many different servers.
Docker and Kubernetes are powerful tools that help you build container-based systems, but they are many more pieces to the microservices puzzle. In addition to using Docker/Kubernetes to create, deploy, and orchestrate the containerized microservices that make up your architecture – you also need a tool that ensures the individual microservices can interact with each other through efficiently-developed API connections.
This is where DreamFactory can help. DreamFactory is an API gateway that allows you to quickly integrate new microservices, applications, and other services into a larger system. By offering the unique ability to automatically generate REST APIs for nearly any database or service in minutes, DreamFactory bypasses weeks of coding time, decreases your labor costs, and achieves a dramatically faster development cycle.
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