What is a Container?
In the world of software engineering, a container is a virtualized environment whose contents are an application and all of the configuration files, libraries, binaries and dependencies needed to execute that application. This method of packaging an application together with its libraries and dependencies enables software development teams to build applications that will perform identically even when moved between computers.
Today's standard methodology for creating and implementing containers was popularized by the technology company Docker who released the open-source Docker application in 2013. Docker is an operating-system-level virtualization software tool that makes it easier for developers to create, test, update, monitor, deploy and run applications using containers. With containers, developers do not have to worry about taking additional measures to ensure that their code functions on a variety of machines.
How a Docker Container is Created
In technical terms, a container is a runtime instance of an image initiated from a DockerFile through the Docker Engine application. Let's cut through the jargon and define exactly how a container is created.
A container is a programming object that consists of an application and all of its dependencies. When working with the Docker Engine, all of the information needed to create and run a container lives in the docker image. A docker image is an ordered collection of root filesystem changes and execution parameters that will be used in the running container.
Images are created using a type of file called a dockerfile. A dockerfile is a text script that contains all of the commands that must be executed to build your docker image just how you want it. After you build the image, running it will launch the docker container and your application will be deployed. We can summarize the whole process as follows:
- Write a dockerfile with instructions telling the docker engine how the docker image should be constructed
- Execute a build command on the dockerfile
- You now have a docker image
- Take your docker image and run it using the docker engine. You have now created a docker container.
Docker Container vs Virtual Machine
Docker containers are frequently compared to virtual machines, as both are examples of virtualization technologies that are implemented during software the testing and debugging process of software developing. Both virtual machines and containers will allow users to package applications together with configuration files and libraries, and provide an isolated environment for running services or applications. Despite their similarities, however, there are a few key elements of difference that set virtual machines and containers apart.
Virtual Machines are More Resource Intensive
The differences in architecture between virtual machines and containers mean that virtual machines are more demanding on computing resources and less efficient than containers.
Both virtual machines and containers require a host machine with hardware and an operating system. Virtual machines use a software called a hypervisor to create and run virtual machines and allocate the physical machine's resources between them. Each virtual machine requires its own "guest operating system", binaries and other dependencies, and application code.
The key feature of containers here is that they don't require a separate operating system to run. A collection of containers on the same physical machine can share the OS kernel, meaning that the user can create several runtime instances of virtualized applications without placing as much burden on the CPU.
Docker Containers Can Be Deployed More Quickly
Docker containers use fewer computing resources than virtual machines because they don't require a separate operating system of their own to function. Another consequence of this is that docker containers can be initiated and scaled up more quickly. When a virtual machine is created by a hypervisor, it takes longer because an entire operating system needs to be started up. This also creates additional resource demands on the system memory that could be avoided by using a container.
Use Cases for Containers in Software Engineering and IT
In today's world of software development, the need to deliver frequent and fast updates to consumers has driven more software developers to adopt the microservice architecture model. In this model, applications are built as a collection of services and each service represents a distinct feature with a clear business value. Containers allow software developers to package each service as an isolated process within the application, streamlining updates and maintenance and enabling continuous integration of new updates.
Containers can be used in conjunction with a specialized function of the Docker Engine known as a Docker Swarm to enhance service or application availability. A docker swarm is a group of physical machines that are working together in a cluster to provide resources to containers. A user can initiate a docker swarm by choosing a swarm manager and assigning other machines to the swarm. When a machine joins the swarm, it becomes a node. There are worker nodes that use their resources to execute tasks and manager nodes that function be allocating tasks or service requests to whichever nodes have resources available.
If one of the manager nodes in a docker swarm fails, the system can automatically recover and continue to assign tasks to worker nodes. Users can implement up to seven manager nodes to ensure that the docker swarm, and therefore the application, remains active even if one or more of the manager nodes experiences an outage.
Application Migration to the Cloud
Using containers makes it easier for software developers to migrate their code to new environments, including cloud environments, without needing code changes to achieve compatibility. Containers also support a standardized code deployment process that streamlines the management of applications that run in hybrid cloud environments.
Complete visibility for DevSecOps
Reduce downtime and move from reactive to proactive monitoring.