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What is the Difference between Kubernetes and Docker Swarm?

 Containerization, along with DevOps processes, has accelerated the ability to build, deploy, and scale applications on cloud systems. Containers have also been a boon to microservices based applications, where the overall application service may comprise two, three, or more smaller applications. The intentional independence of those API-coupled smaller services means each can be updated, scaled up or down, and even entirely replaced as needed. The speed, responsiveness, and flexibility of these systems also bring added complexity that is inefficient if managed by traditional manual IT processes.

Enter the Container Orchestration Engines(COE) like Kubernetes and Docker Swarm.  These are container management automation tools that handle the complexity of web-scale applications with ease.

What is Kubernetes?  

Kubernetes (also referred to as K8s) is a COE that was initially developed by Google based on systems they use to run containers at web-scale and then open-sourced. It can be deployed on almost any kind of infrastructure — anything from your laptop, a local data center, out to the scale of a public cloud.  It is possible to even have a Kubernetes cluster that spans across different infrastructure, such as a hybrid cloud with public and private resources. Kubernetes has a lot of container management “intelligence” built-in that ranges from being able to place containers on the appropriate nodes based on resource requirements, load balancing across applications, scaling applications up and down in response to load, restarting or replacing stalled and failed containers, and more.

What is Docker Swarm? 

Docker Swarm or just Swarm is also an open-source COE. Docker, the company that created the Docker container, also built Docker Swarm as the Docker-native orchestration solution.  One benefit for users of Docker containers, is the smooth integration this provides. For example, Docker Swarm uses the same command-line interface (CLI) as is used for Docker containers. If you are all-in on using Docker for your system, Docker assures backward compatibility with other Docker tools. It is highly scalable, extremely simple to deploy, provides container management features such as load balancing and autoscaling.

Kubernetes vs Docker Swarm 

Though both of the COEs are open-source, run Docker containers, and provide similar functionality there are a number of significant differences in how these tools operate. Below, we’ll look at some of the notable differences and consider the pros and cons of the differing approaches.

 

Installation 

Kubernetes: Installing Kubernetes requires some decisions about, for example, which networking solution to implement, and configuration, at least initially, must be manually defined. Information about infrastructure is also needed ahead of time and includes assigning roles, number of nodes, and node IP addresses. It is worth noting that most major cloud providers have hosted Kubernetes versions that remove much of the friction that building your own system involves.

Docker Swarm: Installing Docker Swarm is about as easy as installing any typical application with a package manager. Creating a cluster just requires you to deploy a node (server) and tell it to join the cluster.  New nodes can join with worker or manager roles, which provides flexibility in node management.

Application (Container) definition

Kubernetes: Applications are deployed as multi-container “Pods” in Kubernetes.  A pod minimally includes the application and a network service container. Multi-container applications can be deployed together in a pod.  Deployments and services provide abstractions that help to manage multiple Pod instances. 

Docker Swarm: Multiples (replicas) of single container applications are deployed and managed as “swarms” in Docker Swarm. Docker Compose can be used to install multi-container applications once those applications are defined with a YAML configuration file.

Container Setup

Kubernetes: Early in its development Kubernetes’ goal was to support a wide range of container types in addition to Docker. As a result, Kubernetes’ YAML, API, and client definitions differ from those used by Docker.  So in addition to running Docker containers, it is also possible to run CRI-O and Containerd runtime-compatible containers on Kubernetes.

Docker Swarm: Docker Swarm was built to run Docker containers and “Docker Ecosystem” tools.  It does not support other container runtimes. Also, it is worth noting that the Docker Swarm API much, but not all of Docker API operations. 

Networking

Kubernetes: Kubernetes integrates with a number of networking technologies with the open-source Calico and Flannel solutions being among the most popular. With flannel, containers are joined via a flat virtual network, which allows all pods to interact with one another with restrictions set by network policy. TLS security is possible but requires manual configuration.

Docker Swarm: Nodes are connected via a multi-host ingress network overlay that connects containers running on all cluster nodes. The Node joining a swarm cluster generates the overlay network for services across all hosts in a Swarm along with a host-only docker bridge network to service containers. It is possible to further configure inter-container networks manually. Authentication (TLS) between nodes is automatically configured.

Load Balancing

Kubernetes: Typical Kubernetes load balancing function is exposed via the concept of a Kubernetes Service that abstracts the function of Pods that comprise the Service. Configuring the service is a manual process that requires a service definition including assigning Pods and policies.

Docker Swarm: Swarm provides automated, built-in internal load balancing. All containers are on a common network that allows connections to containers from any node. Services can be assigned automatically or can run on ports specified by the user. It is possible for users to specify ports to be used for load balancing.

Scalability

Kubernetes: Kubernetes’ focus on reliably maintaining cluster state and the large, unified set of APIs slows down container scaling and deployments as compared with Swarm.

Docker Swarm: Docker Swarm deploys containers extremely fast, even on large clusters, as compared to Kubernetes.

High Availability

Kubernetes: Unless directed to do otherwise, Kubernetes distributes Pods among nodes to offer high availability. Kubernetes detects unhealthy or missing pods and ensures adherence to the desired configuration by appropriately deleting and redeploying Pods.

Docker Swarm: Docker Swarm also offers high availability when services are replicated across a recommended minimum of three Swarm nodes. The Swarm manager nodes in Docker Swarm are responsible for the entire cluster and handle the worker nodes’ resources. Swarm services are self-healing so that if a container or host goes down, Swarm will bring the service back to the desired state.

In a number of other aspects, functions like providing high availability by distributing applications across nodes, self-healing aspects, supporting container updates and rollbacks, and service discovery, specifics differ in detail but functionality is relatively equivalent. The major difference between the two platforms derives from Docker’s focus on ease of use and support specifically for the Docker ecosystem.  Kubernetes’ goal to be as multi-purpose as possible and the ability to select multiple options (e.g. container runtime, networking solution) are seen in an open and modular design that requires more upfront, manual work to get going.

One final consideration in selecting either tool is the trend in popularity, with Kubernetes, with a growing community and support of the CNCF increasingly being the COE of choice. Docker Swarm has not fared as well as, while also open source, was controlled by Docker and later purchased by Mirantis, and has neither seen the growth of strong community support or adoption, as can be seen by proxy in the graph below.

Once you have Kubernetes up and running, updates to configuration continues to be a manual process. Opsani can help with software that applies ML algorithms to provide continuous optimization for Kubernetes systems. What is challenging or impossible for a human, the Opsani AI handily finds and applies optimal configurations to the environment.  Further, Opsani continually refines its understanding of the optimum across time and through load variations. Opsani has been proven to increase performance and save money for businesses that use Kubernetes.

Contact Opsani to know more about how they can help you optimize your infrastructure and cut your costs with the power of AI and ML. You can also sign up for a free trial and experience how Opsani can take your business to higher heights.