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With software making the world go round, the need to get high-quality software running reliably when moved from one computing environment to another has now become essential. Despite differences in supporting environments, network topography, security policies, or storage, the software has to run reliably.

This was a considerable challenge until Containers exploded on the scene and provided the capability to “contain” the entire runtime environment (which consists of the application, all its dependencies, libraries, other binaries, configuration files needed to run it, and are bundled into one package.) This approach helps in abstracting differences in OS distributions and the underlying infrastructure. Containers also useful when the key requirement is modularity. By leveraging the capabilities of containers, the application can be broken up into its constituent modules, making it inherently easier to enable product evolution.

Introducing Docker Swarm

Docker has been synonymous with container technology since its arrival. And the innovations keep coming. Docker Swarm, native clustering for Docker, is a popular open-source standard for packaging and distributing containerized applications. This container application tool allows organizations to manage multiple containers deployed across several host machines comfortably.
One of the greatest advantages of Docker is that it drives high availability of applications as there is at all times, at least one manager node that handles the worker nodes’ resources efficiently and ensures that the cluster operates efficiently at all times.

Introducing Kubernetes

Kubernetes automates many manual processes involved in deploying, managing, scaling, and availability of containerized applications through container orchestration. Simplistically, Kubernetes is an open-source container orchestration platform that sets containers that constitute an app into logical units that facilitate administration and ease of discovery.
Kubernetes has marked a considerable step forward timed perfectly with the rise of DevOps and the need to optimize app development for the cloud. Kubernetes provides a platform to schedule and run containers on a physical or virtual machine (VM). It automates operational tasks and helps developers completely implement and rely on a container-based infrastructure in production environments.

Kubernetes brings many benefits to the table including the capability to orchestrate containers across multiple hosts and maximize the resources needed to run enterprise apps by making better use of hardware. It makes it possible to control and automate deployments and updates, scale containerized applications on the fly, run health-checks, and self-heal applications with auto-placement, auto-restart, auto replication, and autoscaling.

Kubernetes or Docker Swarm – Which is the best choice?

Until now, Kubernetes has been the gold standard to manage containerized workloads. Blessed with a huge support community with numerous contributors, Kubernetes has successfully handled legions of use cases and workloads for numerous organizations. A mature and proven project architecture, Kubernetes is one of the most popular container orchestration platforms.
Docker Swarm has emerged as a worthy Kubernetes contender and quite like it, it manages containers, helps reach the desired state and fixes any deviations from this desired state. Docker Swarm was designed as a ‘mode’ to run Docker and running in swarm mode ensures that the Docker Engine works in concert with other instances of the Docker Engine.

Both Kubernetes and Docker Swarm:
  • Enable teams to specify the desired state of a system using containerized workloads.
  • Employ multiple hosts to form a cluster for load distribution and use containers as units of work. Kubernetes, though, also has the concept of ‘pods’.
  • Orchestrate the needs of the system and keep the system running in a balanced, fault-tolerant manner.
  • Run in both cloud and on-premise. Neither locks you in with a single vendor or cloud platform.
  • Easily list and tail logs into containers, employ tools to aggregate logs and monitor and improve application performance and quality.

Both Kubernetes and Docker Swarm employ Docker containers and, for the most part, only manage the orchestration aspect of applications. Thus, applications employing Kubernetes or Swarm operate with similar efficiencies and speed.
However, Docker Swarm can deploy and start containers up to five times faster than Kubernetes under heavy load. This is mainly because Swarm has fewer moving parts and has tight integration with Docker Engine.

Docker Swarm also responds to APIs faster and performs without any degradation at up to around 90% load, while Kubernetes is said to degrade once it reaches around 50% load. However, the resources used by Kubernetes Pods are minimal and do not have much impact on running the application easily. While this does not impact the application under normal circumstances, it can be challenging to navigate performance milestones in restricted environments that run on low power such as IIoT projects.

Service Discovery

Kubernetes and Docker Swarm both provide DNS-based service discovery. Docker Swarm utilizes a DNS service integrated within the Docker Engine that allows direct calls to containers employing container alias as the domain or using containers assigned to a specific IP address.

Kubernetes maintains its service domains employing KubeDNS Pod and provides numerous ways to reference Pods and services leveraging metadata like labels and annotations. Like Docker, these services can also be referenced by service name. It also provides environment variables for use in referencing Pods and Services in a cluster.
While communicating between services in Docker Swarm is relatively easy, Kubernetes provides more options when dealing with runtime service discovery and cross-cluster service discovery.


Both Docker Swarm and Kubernetes provide a means to apply rolling updates and also for rolling these updates back when needed. However, in Docker Swarm, an update automatically rolls back to the previous version if it fails. A failed Kubernetes update will need an explicit rollback request as a status endpoint does not exist in Kubernetes.

At the same time, selecting Pods, Services, and other assets in deployment using labels and annotations and running staging containers in the wild is much easier in Kubernetes. In the DevOps narrative, this feature makes it easier to roll out a single unit and test it in the production environment before running a cluster-wide update. Performing the same action in Docker Swarm is not quite as easy.

Docker Swarm deployments, however, are considered more user-friendly than Kubernetes. Docker Swarm, contrary to some popular opinions, can also support large volumes of nodes and containers. But, Docker Swarm has a task limit. Kubernetes, on the contrary, can support almost ten times this limit making it well suited for large projects.

So, Kubernetes or Docker Swarm, which one is the right choice for you? Both the tools have their set of advantages for their users. While Kubernetes is perfect to support higher demands with greater complexities, Docker Swarm is simple to get started with. Kubernetes is wildly popular in production environments while Docker Swarm is popular with developers for faster deployments and its simplicity. While the objective of both the tools is to deal with issues of container orchestration, both do the same taking somewhat different approaches. Making a choice between the two depends on the application needs, proficiency in technology, and the product evolution roadmap.

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Shivaji Mutkule | Lead Software Engineer

Shivaji has 7+ years of experience in Software Development. He is an experienced FullStack Developer and works on cutting-edge technologies in the healthcare domain. Shivaji possesses industry experiences in Web Development, Analytics, Microservices, DevOps, and Azure Cloud Networking. He has completed M.E. in Computer Science and Engineering from Government College of Engineering, Aurangabad(GECA). His area of interest includes Web development, Data Mining, and Machine Learning.

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