Kubernetes is defined as an open-source container orchestration solution leveraged for the management of containerized services and workloads. Docker is defined as a framework used to build, operate, and manage containers on the cloud and on servers. This article covers the key comparisons between these two containerization platforms.
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Kubernetes vs. Docker
Kubernetes is an open-source container orchestration solution leveraged for managing containerized services and workloads. Conversely, Docker is a framework used to build, operate, and manage containers on the cloud and servers.
Kubernetes Cluster (top) vs. Docker Architecture (bottom)
Before we dive into the detailed definitions of these two platforms, let’s understand more about the solution both are built to support: containers.
What is a container?
A container can be defined as a small-scale virtual machine. This software unit contains a comprehensive bundle of code and all the dependencies required for its execution. This allows the application ‘contained’ to run independently, swiftly, and effectively on any computing platform.
Containers operate atop the shared operating system on a host machine. However, they run independently from one another unless linked by users. For more scalable, consistent, and secure workloads, software teams rely on containers.Â
Containers are not as resource-intensive as full-scale virtual machines, which virtualize an entire operating system environment and enable users to run all compatible software programs.
Now that we understand what a container is, we can move toward understanding what Kubernetes and Docker are.
What is Kubernetes?
Kubernetes is a popular open-source container orchestration platform. Also known as K8s (for the eight letters between K and S), this solution is leveraged for container management across public, private, and hybrid cloud platforms. Apart from this, it is a useful tool for managing microservice architectures.
Advantages of Kubernetes
Software developers, DevOps engineers, and sysadmins leverage Kubernetes to automate the deployment, scheduling, scaling, operation, monitoring, and maintenance of large-scale container architectures, typically in clustered arrangements.
The advantages of Kubernetes are:
- Deployment: Kubernetes allows users to choose, configure, and modify the states for the deployment of containers. This includes the creation of new container instances, migrating existing containers, and deleting old containers.
- Equitable traffic distribution: The platform can perform load balancing operations, wherein traffic is equitably distributed across numerous container instances.
- Supervision: With Kubernetes, users can constantly monitor container health. One can restart malfunctioning containers to fix them and, failing that, remove them.
- Intelligent automation: Kubernetes infuses container deployments with a layer of intelligence, allowing for useful features. For instance, resource optimization functionality highlights the available nodes, identifies the resources required for container operations, and automatically connects those nodes and containers.
- Data storage: The solution supports the storage of container data across several storage types, including local, cloud, and hybrid.
- Cybersecurity: Finally, Kubernetes is capable of the secure management of passwords, SSH keys, tokens, and other critical data.
Disadvantages of Kubernetes
While Kubernetes is undoubtedly an amazing tool for container-enabled enterprise architectures, it does have a few drawbacks, such as:
- Complexity of operations: The distributed nature of container management through Kubernetes is useful for enhancing scalability and flexibility. However, introducing large-scale containerization often leads to the increased complexity of IT operations, which might impact availability in cases of incorrect configuration.
- Professional proficiency: Adopting Kubernetes normally requires changes in the existing roles and responsibilities within the IT environment of an organization. A key factor influencing this decision is the chosen storage model for deployment: Public cloud deployments and on-premises server deployments require different specializations. Apart from this, the size of the enterprise, the number of technical personnel, and the infrastructure and scalability of the organization must be considered while adopting Kubernetes.
- Scaling under load: Some container applications can scale differently or even fail to scale when exposed to high loads. Users must be mindful of the methodologies used for node and pod balancing.
- Limited observability: While Kubernetes can monitor large-scale container deployments, human supervision of all production workloads becomes more difficult as the architecture scales up. Monitoring the different layers of the Kubernetes stack to ensure optimum security and performance can become a challenge for widespread deployments.
- Security concerns: Container deployment in a production environment requires enhanced cybersecurity and compliance measures. This includes multi-factor authentication, analysis of code vulnerabilities, and concurrent handling of numerous stateless configuration requests. One can assuage concerns around Kubernetes security through correct configuration and proper setup of access controls.
What is Docker?
As discussed above, a container includes all the components required for an application to operate in isolation. Docker is used in creating such application containers.
This platform is popularly leveraged to decouple applications and frameworks, allowing the former to run in any environment. The key advantage of Docker is its ability to enhance the delivery speed of applications and operating systems. Docker was developed in 2013 and is a pioneer of the modern container movement.
Docker allows users to manage their frameworks similarly to how they operate their applications. Its core functionalities include the effective development, shipping, and operation of applications, achieved by automating application deployment in lightweight, portable containers.
When used for testing, shipping, and code deployment, Docker can minimize delays in the code writing process and enhance the speed at which one can set up the application to run in production.
Advantages of Docker
Docker is so widely used in the industry today that its name has become synonymous with the word ‘container’. With Docker, developers can use simple commands to access containerization capabilities. Additionally, one can use effort-saving APIs to automate container operations.
Here are a few other advantages of Docker:
- Enhanced portability: Docker containers can be deployed on any data center, cloud environment, or endpoint–no modifications are necessary.
- Modular architecture: The Docker framework allows users to integrate multiple processes into one container. This enables the creation of applications that can be operated even while some components are being repaired or updated.
- Cross-platform functionality: Apart from Linux, Docker containerization is compatible with popular operating systems such as Windows and macOS. Users can also deploy Docker containers on leading cloud platforms such as Azure, AWS, and IBM Cloud. These cloud providers offer dedicated services that one can use to create, deploy, and operate applications that have been containerized using Docker.
- Container templatization: Docker supports using existing containers as base images. These base images serve as templates for the creation of new containers.
- Automated provisioning: Docker can use the application source code to set up containers automatically.
- Container library: Users can access thousands of containers created and shared by others through an open-source registry.
- Versioning: Docker can track the versions of container images, initiate version rollbacks as necessary, and keep records of version creation. Delta uploading between existing and new versions is also possible. Finally, programmers using Docker are free to develop containers in different language versions without damaging other lines of code.
Disadvantages of Docker
As with every technology, Docker has its challenges; for instance, monolithic applications may face compatibility issues with Docker containers.
Here are a few other disadvantages of Docker:
- Upskilling duration and effort: Gaining proficiency in Docker is a time-taking process, and newbies face a steep learning curve. Knowledge of Linux is also required for customizing or maintaining the Docker Engine.
- Cross-platform communication: While Docker containers communicate with each other without any problems, data transfer between Docker containers and containers by rival container companies may not always be seamless. This can lead to challenges in environments where developers require more than one container.
- Lack of persistence: Critics might point out that Docker’s high portability and modularity can lead to occasional issues with persistent storage. Unless Volumes is set up for data storage in the Docker Engine, a container that has completed its assigned process will shut down and make all processed data inaccessible. Currently, no automated process exists for addressing this issue.
- CLI reliance: Docker operations rely on proficiency in command-line interface (CLI) usage, and the framework is optimized for applications that operate using terminal commands. This can lead to issues for users working with applications that require a graphical user interface (GUI).
See More: What Is Data Fabric? Definition, Architecture, and Best Practices
Key Comparisons: Similarities and Differences
We have covered Kubernetes and Docker’s definitions. Let’s now understand the similarities and differences between these two solutions.
Similarities between Kubernetes and Docker
A key similarity between Kubernetes and Docker lies in the fact that they are both open-source frameworks. They are also cloud-compatible, with most leading cloud vendors supporting both platforms’ features and functionality. Finally, as established extensively in this article, they’re both related to containers.
It would be unfair to frame Kubernetes and Docker as competitors–they are distinct solutions that work better together to create, scale, and deliver containerized applications.
Users can easily operate a Docker build on a Kubernetes cluster. However, Kubernetes is not an independent solution. It needs to be optimized in production by implementing additional components for managing identity, access, cybersecurity, and governance. Additionally, one must take measures to implement DevOps practices such as continuous integration and continuous deployment (CI/CD) workflows.
Apart from the technology, Docker is also the name of a container file format that automates application deployment in the form of portable, independent containers. And even though Docker, Inc. has the same name as the technology and the file format, it is a distinct company that gives users access to Docker technology on Linux and Windows platforms in partnership with leading cloud providers.
Similarly, Kubernetes is well-established throughout the container landscape as an orchestration solution. The Kubernetes API gives users control over how, when, and where their containers work. Kubernetes helps mitigate some of the operational complexities faced by users looking to scale up multiple Docker container deployments across numerous servers.
Kubernetes and Docker can team up to fulfill common container-centric goals in any organization with a relevant use case. For instance, they enhance the availability and strength of application infrastructure. This will allow apps to stay online even if some nodes stop responding.
Additionally, they’re useful for boosting application scalability and implementing effective load balancing measures. If an application needs to scale up because of an increase in traffic inflow, these solutions will generate more containers or nodes within the cluster, as required.
Differences between Kubernetes and Docker
At the highest level, Kubernetes is the step after Docker–the container orchestration platform lets users run Docker containers and addresses operating complexities at scale.
The differences between Kubernetes and Docker are:
| 1. Architecture | |
| Kubernetes | Docker |
| The components listed below are a part of a comprehensive Kubernetes architectural environment:
Pod: A pod is a grouping of containers. Each pod is assigned one IP address, which is shared across containers in that pod. Apart from this, all containers in a pod also share resources such as RAM and storage. This allows them to operate as one application collectively. Pods can even hold a single container for executable applications that are one process. Pods normally feature ephemeral storage, losing all data when replaced or destroyed. Deployment: Deployments are leveraged when specifying the scale of operations for the application. The number of identical pod replications for execution and the preferred strategy for deployment updates are defined in this architectural component. Users on the Kubernetes nodes must define pod replication preferences. Service: In a Kubernetes environment, pods are expendable and transitory. When a pod is destroyed, Kubernetes replaces it with another to prevent application downtime. Service is an abstraction over the pods and serves as the interface for users to engage with. Services help networks connect with pods even when they are internally assigned new names and IP addresses. Node: A node is a machine, either physical or virtual, which is responsible for the execution of the assigned task through pod management. A node is a collection of pods that execute commands together, similar to how pods are groupings of containers with common operative parameters. In large-scale container infrastructures, tasks are assigned to the nodes and then delegated to the pods as per bandwidth availability. Kubelet, Kube proxy, and etcd are critical components of the worker node. Control plane: The control plane is the point of access for administrators and other Kubernetes users. It is the component used for effective node management and controls how Kubernetes interacts with other applications. Operations assignment at the control plane level occurs either by connecting to the machine and running command-line scripts or over HTTP calls. The control plane contains the API server, controller manager, and scheduler. Cluster: Finally comes the cluster–an assemblage of the other components. This component is a collection of nodes equipped with Kubernetes and sharing the same computing resources. One can scale out clusters by adding more nodes to the existing ‘node pool’. Scaling out a cluster leads to redistributing pods and creating a configuration that accounts for the new nodes. Leading cloud providers automate cluster management, with the number of nodes and physical specifications being the only inputs required. |
Docker uses a client-server architecture containing the following components:
Daemon: The daemon (server) runs containers and manages Docker services at the host operating system level. It is also responsible for communicating with other daemons and offering containers, images, and other Docker objects. Client: Communications between the client and the daemon occur through commands and REST APIs. When a command is executed on the client terminal, it is forwarded to the daemon. The client can communicate with multiple docker daemons. It also uses a command-line interface to execute commands, including docker build, docker pull, and docker run. Host: The host provides a platform for application execution. This architectural component includes the daemon, containers, images, storage, and networks. Registry: Docker images are managed by and stored in the registry. A container registry holds images for sharing within an enterprise (private) or worldwide (public). Finally, the Docker Objects are listed below:
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| 2. Key Features | |
| Kubernetes | Docker |
| Developed by Google in 2014, Kubernetes features a simple cluster setup that needs a few commands for normal operations. However, its installation process is complex and can be time-consuming.
With Kubernetes, the sharing of storage volumes is possible across containers. The solution can support 5,000 nodes and 300,000 containers. Finally, it features auto-scaling and in-built monitoring. |
Developed by Docker Inc in 2013, Docker features strong clusters that can be challenging to set up. However, its installation process is swift and simple.
With Docker, sharing of storage volumes is only possible among the containers within the same pod. Additionally, there is no scope for auto-scaling. The solution can support 2,000 nodes and 95,000 containers. Finally, third-party tools such as Kubernetes are required for monitoring in Docker. |
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| 3. Applications | |
| Kubernetes | Docker |
| The primary application of Kubernetes is the management and consolidation of containers. Apart from this, enterprises use this platform to manage tokens, passwords, SSH keys, and other secure data.
Kubernetes is also used to enhance service discovery. The automatic detection and customization of service discovery for network-wide containerized applications is a key application of this solution. It is also used for managing multi-cloud and hybrid cloud platforms and extending local workloads across multiple cloud platforms. This helps businesses enhance resiliency and availability. Additionally, it gives businesses the flexibility to choose varying service configurations. Expanding platform as a service (PaaS) options is also an application of Kubernetes. Its ability to support serverless workloads has the potential to introduce new types of PaaS options, which can enhance reliability and scalability and minimize costs. Finally, enterprises can leverage an existing Kubernetes deployment in their datacenters and clouds to extend the capabilities of their edge computing setups. Such a configuration might include a small-scale server farm in the cloud or outside a data center setup. One can use Kubernetes to deploy, manage, and maintain edge computing and IoT components, as one can pair them with application components at the datacenter level. |
The core application of Docker is the creation of standalone objects that one can execute reliably on any platform. Its simple, user-friendly syntax allows Docker to exercise great control over enterprise applications.
Docker containers are used as building blocks for the creation of cutting-edge applications. Apart from this, the creation and execution of distributed microservices architectures become simplified with Docker. The platform is also useful for deploying code through standardized CI/CD pipelines, building scalable data processing platforms, and creating managed development platforms. For instance, a 2020 collaboration initiative between Docker and AWS has simplified the deployment of Docker Compose artifacts to AWS Fargate and Amazon ECS. The average Docker user can ship software with a frequency up to seven times higher than that of a non-Docker user. Additionally, one can ship isolated services as often as required. Creating tiny containerized applications also allows for easier deployment, identification of issues, and rollbacks. Cost-saving is another lucrative application of Docker containers, as they simplify the execution of higher volumes of code on each server to boost utilization rates. Finally, the solution features wide adoption, a robust tools ecosystem, and a range of off-the-shelf, turnkey applications, making it an ideal choice for container-powered software enterprises. |
See More: CI/CD vs. DevOps: Understanding 8 Key Differences
Takeaway
Kubernetes and Docker are both leading cloud-compatible solutions that are popularly used for creating, managing, and orchestrating containers in enterprise environments. While they share many similarities, they differ primarily in architecture, applications, and critical features.
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