Containerization offers an adaptable technology with a diversity of applications for both DevOps and IT teams. When containerization is properly applied, it accelerates deployment; increases the efficiency of DevOps; minimizes infrastructure issues, and streamlines workflows. Containerization also enables better use of available resources by developers. Containers can be configured to take full advantage of all virtual computing resources available, requiring practically no operational overhead.
The containerization concept dates back several decades, but a renaissance for containers has been sparked by the introduction of tools like Docker Engine and Kubernetes. As a result, this technology has been catapulted to the forefront of many development workflows. We predict that containerization will see many more uses in the future, especially as applications continue to evolve in sophistication. For those who haven’t already begun developing with containers, now is a better time than any to start.
Container software is an ideal option for today’s software developers, greatly simplifying many problematic issues. This guide assumes that the reader is already somewhat familiar with container software and using tools such as Docker, but serves as a concise guide for new developers or those who wish to refresh their knowledge.
What Is a Container?
Containers work as a form of operating system virtualization (OS virtualization). They’re quite similar to virtual machines, virtualizing system singular resources like Disk, RAM, GPU, CPU, or networking while representing them as multiple resources. However, unlike machine and server virtualization solutions, containers don’t contain images of operating systems.
As a result, containers are significantly more lightweight and portable with relatively less overhead. Containers achieve this by packaging up code and all dependencies so that applications run fast and reliably between computing environments.
Single containers can be employed to run anything from tiny software processes or microservices to more comprehensive applications. All the necessary configuration files, executables, libraries, system tools, runtime, settings, and binary code are in a container as well.
Multiple containers are deployed as one or more container clusters to facilitate more extensive application deployments. Containerization platforms such as Docker, CoreOS rkt, or Container Linux are used to run containerized programs.
At runtime, unchangeable, static files called “container images” become containers when the runtime environment is initiated. For example, Docker images become containers when the Docker Engine starts. Containerized software is available for both Windows and Linux-based applications and always runs the same. No matter the differences between development and staging, software runs uniformly because containers isolate it from the host environment. According to Docker, these are the containers that run on Docker Engine:
- Standard: An environment for running processes with resource limitations and configurable isolation.
- Lightweight or Microcontainers: Share a physical machine’s OS system kernel and don’t require an OS per application. This helps with server efficiency while reducing licensing and server costs.
- Secure: Applications are inherently safer running in containers. Docker has a reputation for providing the most robust default isolation capabilities in the industry.
The Benefits and Attributes of Container Software
Containers are designed to download fast and start running immediately. In addition to that, containers normally use less memory and computing power than virtual machines like VirtualBox and VMware. Container images have a virtual size calculated in terms of distinguishable underlying layers and differentiated through the initial 12 characters of a true identifier. You can either tag an image or leave it untagged, making it searchable only through the true identifier.
Containers are broadly interoperable because they operate across varied infrastructures, such as physical and virtual machines, as well as cloud-hosted instances. Furthermore, applications in container software deployments are abstracted from the underlying infrastructure and isolated from one another. This isolation protects the underlying infrastructure from any possible negative effects caused by the container image or potentially malicious software.
Other benefits include:
- Better Application Development: Containers promote DevOps and agile endeavors to accelerate application development, testing, and production.
- Rapid Startup Times: Containers are extremely lightweight compared to alternative virtualization solutions such as virtual machines. This allows for rapid start-up times since containers don’t depend on a virtualized or hypervisor operating system to access necessary computing resources. To put it another way, start-up times are virtually instantaneous, no pun intended. The application itself is pretty much the only limiting factor one would have to consider. This is because your code will determine the start-up delay. Lastly, frequent improvements and updates are a lot easier with rapid start-ups.
- More Consistent Operation: IT and DevOps teams know without a doubt that their applications will run the same in containers. They don’t have to worry about where they’re deployed.
Downsides Associated With Using Containers
IT teams must keep an eye out for container images from unscrupulous sources. This requires management to train their team about best practices for pulling images from public repositories. However, this doesn’t guarantee that someone won’t accidentally pull a fraudulent image eventually. At the end of the day, it’s in the hands of the individual who pulls the container image to do so responsibly. Enterprise IT organizations can avoid these mistakes by creating a limited list of available container images.
Along with authenticity, organizations have a difficult time managing container sprawl. Container sprawl is defined as an organization’s propensity to collect an excessive number of container images. The problem of cost appears to be the same no matter if one runs a physical data center or a cloud-native container system. Management issues, development inefficiencies, and exuberant cloud computing fees are the most common results of spinning up too many containers.
It’s important to understand that despite the convenience provided by creating containers versus setting up physical servers, costs can quickly explode. Organizations can avoid this by not collecting numerous containers that essentially accomplish the same things. Additionally, delete container images that are no longer needed — stopped containers aren’t removed automatically, continuing to take up valuable storage space. The good news is that docker rmi and docker rm can be used to delete unused containers and images.
Types of Container Software Images
Users create container images from scratch using the build command in Docker. They can also update containers over time and modify images to employ as the foundation for new containers. Developers can also fix bugs and make other changes to the software as needed. Users define a set of layers assembled into images in a Dockerfile for increased automation.
Each command a user creates in a Dockerfile constructs a new layer in an image. Container image builds can also be automated with continuous integration tools like Jenkins. Jenkins is an open-source automation server that allows users to automate the aspects of software development associated with building, testing, and deploying. In addition to that, it fosters continued integration and ongoing delivery.
Many images of products developed by software companies or sold by software vendors are publicly available, and many are free to download. Some examples include Docker Hub, which provides the world’s largest repository of container images, and Microsoft’s SQL Server 2017 container image that runs on Docker.
Containers are also stored on private registry servers on Docker, CircleCI, Buildah, LXD (Linux daemon by Ubuntu), Containerd, and a few others. While some container images are large in file size, many others are purposefully minimal. Even for those container images that grow to multiple gigabytes in size, there are ways of decreasing their size significantly. For instance, you could reduce a 1.43 GB Docker image to merely 22.4 MB, according to this article published in JavaScript in Plain English.
Image creators push their container images to the registry, while users pull image containers when running them. If you’ve ever used GitHub, you’re familiar with the “push” and “pull” system.
However, one should be cognizant of malicious, corrupt, and counterfeit container images made publicly available to unwitting container adopters. These dangerous container images are usually disguised as an official vendor’s image.
To help users verify that image files on public repositories are original and remain unchanged, digital signatures are used by PaaS providers. For example, Docker features Content Trust. Nevertheless, such added safety measures don’t prevent disreputable actors from creating and distributing container images infested with malware or spyware.
Types of Container Runtime
Another term for a container engine is container runtime. It’s the software that runs containers on host operating systems. They load container images from repositories and manage the container lifecycle, as well as monitor and isolate local system resources. Standard container runtimes run with container orchestrators. Orchestrators manage container clusters and handle scalability, security, and networking.
Container engines are responsible for each container running on compute nodes within a cluster. The most commonly used container runtimes include Docker, Windows Containers, and runC. Additionally, there exist three primary container runtime types:
1. Low-Level Container Runtimes
Docker initiated the Open Container Interface (OCI) which is a Linux Foundation project. OCI strives to deliver open standards for Linux-based containers. Released in 2015, OCI’s main open-source project is runC, a low-level container runtime that executes the OCI specification.
These specifications are referred to as “low-level” runtimes because they are the chief priority in container lifecycle management. Runtime containers are created by native low-level runtimes. The container runtime isn’t needed to complete additional tasks once the containerized process runs. Low-level runtimes aren’t designed to perform additional tasks because they abstract the Linux primitives.
The most prevalently employed high-level and low-level runtimes include:
- containerd: This is an open-source daemon supported by Windows and Linux. It uses API requests to manage container life cycles; the container API improves container portability while adding a layer of abstraction.
- runC: Written in Go, this low-level container runtime has become the standard among many developers. It’s maintained under Docker’s open-source project called moby.
- crun: Led by Redhat, this low-level runtime is implemented by OCI. It was among the first runtimes to support cgroups v2. It’s designed to be performant and ultra-lightweight.
2. High-Level Container Runtimes
- Docker: Offering both free and paid options, containerd is the default Kubernetes container runtime and the foremost container system that offers a full suite of features. It provides container image-building services, image specifications, and a command-line interface (CLI).
- CRI-O: An OCI-based implementation of Kubernetes container runtime interface (CRI) optimized for Kubernetes deployment. OCI is a lightweight alternative to Docker and rkt that provides support mainly for Kata and runC. However, any OCI-compatible runtime can be plugged in.
- Windows and Hyper-V Runtimes: Available on Windows Server, these are two lightweight alternatives. Similar to Docker, Windows Containers provide abstraction; Hyper-V offers virtualization. You can run incompatible applications in your host system because they each have their own kernel. As a result, Hyper-V containers are easily portable.
3. Virtualized and Sandboxed Runtimes
- Virtualized Runtimes: Delivers improved host isolation by running containerized processes in virtual machines instead of a host kernel. However, running containers in a virtual machine makes the process noticeably slower as compared to native runtimes. OCI-compatible Kata Containers (previously Clear Containers) and the hypervisor-based runV (also for OCI) are a couple of examples.
- Sandboxed Runtimes: Delivers improved isolation between the host and the containerized processes. Processes run on a kernel proxy layer or unikernel since they don’t share a kernel. As a result, the attack surface is reduced. A couple of examples include nabla-containers and gVisor developed by Google.
Container Orchestration and Management Tools
Many container orchestration tools are available that offer a framework for running containers and microservices architecture at scale. Additionally, many container orchestration tools provide lifecycle management. A few of the most popular examples include Apache Mesos, Docker Swarm, and Kubernetes. Originally developed and designed by engineers at Google, Kubernetes is the most popular choice among developers today. Several of the primary Kubernetes services and platforms to pay attention to include:
vFunction Helps You Modernize to Prepare for Containerization
Enterprise IT and DevOps teams use containers to develop and deploy applications more quickly, easily for hybrid cloud environments, as well as scale efficiently and automatically. Most importantly, container platforms like Docker provide enhanced security while making deployment easy and reliable. Using containers with Kubernetes for orchestration and management greatly improves the process.
To move your organization’s monoliths into a cloud native architecture that can use containers, you must consider a transformational type of application modernization to obtain a notable strategic advantage. This requires breaking up legacy Java applications into microservices and establishing CI/CD pipelines for deployment. In other words, it’s all about making your applications cloud-native.
However, it can be fairly risky to carry out a full-scale modernization of that kind without a recipe for success and tools to simplify the process, and you’ll be confronted with numerous challenges and have many choices to make. The process involves a lot of hard work and dedication. Thus, it’s likely you’ll want a little help. vFunction offers a platform that automates and accelerates refactoring legacy applications into cloud-native architectures. Our platform enables organizations, development teams, and IT operations to take full advantage of all the benefits brought by containers, Kubernetes and cloud-native infrastructure. vFunction has developed a repeatable platform capable of transforming legacy applications safely, quickly, and consistently. Request a demo today to see for yourself how we can help with your transformation.
