Containers are self-contained applications and services that include all their dependencies, therefore making them easily deployable and updatable. They ensure the application runs quickly and reliably from different environments. A Docker container image is a package of software containing the application, in addition to the dependencies required to run the application, such as code, runtime, and settings.
During runtime, container images become containers. And when it comes to Docker containers, images that run on Docker Engine become containers. The infrastructure does not affect how containerised software runs, and it is available for Linux and Windows-based applications. Containers give an additional layer of protection by isolating applications from their host and each other. They also reduce the hosts surface area.
Docker containers that run on Docker Engine are:
- Standard: The containers are portable because Docker created the industry standard for containers.
- Lightweight: Containers do not need an OS per application, seeing as they share the machine’s OS system kernel.
- Secure: Docker provides extensive default isolation capabilities making applications safer in containers.
In 2013, the launch of Docker container technology as an open-source Docker Engine took place democratising software containers. Docker Engine is the lightweight application runtime tool with built-in features to build, manage and deploy single or multi-container applications.
Docker containers help simplify application development and deployment. Leveraging existing computing concepts around containers, specifically in the Linux department, Docker’s technology focuses on the requirements of developers and systems operators to separate application dependencies from infrastructure.
Docker containers are portable across development, test and production environments. You can install Docker Engines on any physical or virtual host that runs a Linux OS in a private data centre or cloud. The deployment of Docker containers then takes place to run across the collection of Docker Engines.
Microsoft brought Docker containers to Windows Server (also known as Docker Windows containers) after the Linux success.
Docker developed a portable, flexible and easy to deploy Linux container technology. They open-sourced libcontainer and partnered with a worldwide community of contributors to further its development. The container image specification and runtime code, known as runc, was donated to the Open Container Initiative (OCI) in 2015 to assist in establishing standardisation during the growth of the container ecosystem.
Docker still gives back with the containerd project, which was a donation to the Cloud Native Computing Foundation (CNCF) in 2017. Containerd leverages runc and is an industry-standard container runtime. It provides you with an open and extensible base for building non-Docker products and container solutions.
- Why Kubernetes?
Kubernetes is a transportable, extensible open-source platform that individuals use to manage containerised workloads and a variety of services. The platform additionally simplifies declarative configuration and automation. Furthermore, it has a growing ecosystem meaning that any support and services required, as well as tools needed, are readily available.
It is essential to look at the actual name, Kubernetes, to gain an in-depth understanding of its origins. The name derives from Greek, and it essentially means helmsman or pilot. Google open-sourced the Kubernetes project in the year 2014. Kubernetes, along with 15 years of Google’s experience running it, forms advanced production workloads at scale, which leads to the best-of-breed ideas and practices.
In the past, if we look at traditional deployments, companies operated applications on physical servers. Individuals did not have a way to define resource boundaries for apps in physical servers which ended up causing allocation issues. An example of this is multiple apps running on the same server, which caused instances of one application taking up the majority of resources. When this happens, the other applications underperform. Teams need to avoid this problem by running each application on a separate physical server. However, scaling does not take place as resources end up being underutilised and costly for organisations to maintain.
If we have a look at the virtualised deployment era, this was where the introduction of virtualisation came in, which is a system that allowed teams to run multiple Virtual Machines on a single server’s CPU. You had the option to isolate applications on different Virtual Machines (VMs), and security was tight. No information could be accessed freely by another application.
Virtualisation provided teams with a more convenient way of utilising resources in physical servers. There was also an increase in scalability because apps could be updated and added easily with hardware and maintenance costs reduced. Physical resources can be presented as clusters of disposable virtual machines. So, in theory, each VM is like a full machine running many different components which include the operating system, and all the virtualised hardware.
Stepping away from the virtualised era to the container deployment era brought about agile application creation. Containers were similar to VMs. However, they shared operating systems (OS) with apps via relaxed isolation properties. Due to this, containers are lightweight. Similarly, they also have their filesystems, CPU, process spaces and memory (just like virtual machines). Users also like container deployment because they are portable across clouds and OS distributions.
Containers are accessible for several reasons. Some of these include:
- The efficiency of container image creation when compared to virtual machine image creation.
- Containers allow for continuous development, integration and deployment, making them reliable and ensuring quick and easy rollbacks for image building and implementation.
- The Dev and Ops allow users to create container images at the build and release time instead of the deployment time (which assists with decoupling apps from the infrastructure).
- Observation is critical, and containers allow for surface OS-level information and metrics, including health status and other signals.
- Environmental consistency is done across the development stages, testing and production. Running applications do not change when devices are switched.
- The distribution portability is excellent with cloud and OS having the ability to run on Ubuntu, RHEL, CoreOS.
- Applications that are broken into smaller separate pieces are deployed and managed dynamically. The distribution and liberated micro-services allow for work flexibility.
- Teams receive predictable application performance with resources that are high in efficiency and density.
Containers are an excellent way for teams to run their applications. Within a production environment, you need to manage your applications to ensure that no downtime takes place.
Kubernetes allows your team to have a framework that enables them to run distributed systems resiliently. Scaling for applications as well as failover, is all taken care of while providing deployment patterns to allow for easy management of apps. Kubernetes provides your team with an array of advantages, such as service discovery, storage orchestration (automatically mounting any system of your choosing), having automated rollouts (creating new containers automatically for your deployment), automatic bin packing, self-healing (restarting containers that fail and replacing containers) and configuration management (allowing you to store and manage sensitive information)
Kubernetes vs. Docker
When we think about Docker, containers immediately comes to mind. Containers assist developers when they are working on code in their local development environment, therefore, solving critical issues in application development. However, problems usually come into play when developers want to move code to production. More often than not, code that previously worked on the developer’s machine does not work in production. Reasons for this vary, ranging from operating systems to different dependencies.
Containers fixed these issues of portability by allowing teams to separate code from the underlying infrastructure that it runs. So, developers now had the option of storing their apps, including bins and libraries, into small container images. When production time came, containers could be run on any computer that has the containerisation platform installed.
Containers assisted developers in solving the issues of portability. In addition to this, container platforms and individualised containers come with an array of advantages when compared to traditional virtualisation.
Containers do not require a lot to run. All they need is the application as well as a definition of all the bins and libraries. Container isolation does not require a guest operating system like a VM needs. Everything for a container is done on the kernel level. Libraries can also be accessed across different containers which eliminate the need for having ten copies of the same library on a server. Applications that are condensed in their self-contained environments allow your teams to have quicker access, faster deployments and infinite scalability.
When looking at Docker, it is essential to note that it is one of the most popular container platforms. First appearing on the market, Docker was open source from the start, which inevitably led to its market domination in the field. As many as 30% of enterprises make use of Docker in AWS environments.
Docker engine is linked to Docker, and this is the runtime that allows users to build and run their containers. Before running a Docker container, a Docker file needs to be established. A Docker image is built after the Docker file, and it is a portable, static component that runs on the Docker engine.
Coordinating and scheduling containers is a challenge. However, a solution for orchestrating uninterrupted services emerged with Kubernetes, Mesos, and Docker Swarm.
Docker Swarm administers large numbers of containers across many different smaller servers while Dockers solution includes having tightly integrated cluster solutions in the ecosystem of Docker.
Kubernetes is made up of many components that all talk to each other through the API server. Components all operate their functions which creates a control loop for users to state the appearance of their systems and how they want them to run.
Docker and Kubernetes both have intelligent solutions for containerised applications and provide users with powerful capabilities. Docker assists with the tools for building, distributing and running Docker containers. It is used for scheduling and orchestrating containers on machine clusters.
Kubernetes is an orchestrating system for Docker containers. It coordinates clusters of nodes at large scales during the production phase. Both are very different technologies; however, they work very well together.