Kubernetes vs Docker Swarm : Comparing Container Orchestration Tools

Learn the difference between Kubernetes and Docker Swarm with when to use each.

when deploying application at scale , you need to plan and co-ordinate all you architecture components with current and future strategies in mind. "Container Orchestration tools help achieve this by automating the management of application Microservice across multiple clusters. two of the most popular container orchestration tools are kubernetes and Docker Swarm.

Let's explore the major feature and difference between Kubernetes and Docker Swarm in this Article.

Kubernetes Overview

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available.

The name Kubernetes originates from Greek, meaning helmsman or pilot. Google open-sourced the Kubernetes project in 2014. Kubernetes combines over 15 years of Google's experience running production workloads at scale with best-of-breed ideas and practices from the community.

• Immutable infrastructure is a practice where servers, once deployed, are never modified. If something needs to be changed, you never do so directly on the server. Instead, you’ll build a new server from a base image, that have all your needed changes baked in. This way we can simply replace the old server with the new one without any additional modification.
• Containers offer a way to package code, runtime, system tools, system libraries, and configs altogether. This shipment is a lightweight, standalone executable. This way, your application will behave the same every time no matter where it runs (e.g, Ubuntu, Windows, etc.). Containerization is not a new concept, but it has gained immense popularity with the rise of microservices and Docker.

Armed with those concepts, we can now define Kubernetes as a container or microservice platform that orchestrates computing, networking, and storage infrastructure workloads. Because it doesn’t limit the types of apps you can deploy (any language works), Kubernetes extends how we scale containerized applications so that we can enjoy all the benefits of a truly immutable infrastructure. The general rule of thumb for K8S: if your app fits in a container, Kubernetes will deploy it.

By the way, if you’re wondering where the name “Kubernetes” came from, it is a Greek word, meaning helmsman or pilot. The abbreviation K8s is derived by replacing the eight letters of “Kubernete” with the digit 8.

The Kubernetes Project was open-sourced by Google in 2014 after using it to run production workloads at scale for more than a decade. Kubernetes provides the ability to run dynamically scaling, containerised applications, and utilising an API for management. Kubernetes is a vendor-agnostic container management tool, minifying cloud computing costs whilst simplifying the running of resilient and scalable applications.

Kubernetes has become the standard for running containerised applications in the cloud, with the main Cloud Providers (AWS, Azure, GCE, IBM and Oracle) now offering managed Kubernetes services.

Kubernetes basic terms and definitions

To begin understanding how to use K8S, we must understand the objects in the API. Basic K8S objects and several higher-level abstractions are known as controllers. These are the building block of your application lifecycle.

Basic objects include:

• Pod. A group of one or more containers.
• Service. An abstraction that defines a logical set of pods as well as the policy for accessing them.
• Volume. An abstraction that lets us persist data. (This is necessary because containers are ephemeral—meaning data is deleted when the container is deleted.)
• Namespace. A segment of the cluster dedicated to a certain purpose, for example a certain project or team of devs.

Controllers, or higher-level abstractions, include:

• Replica-Set (RS). Ensures the desired amount of pod is what’s running.
• Deployment. Offers declarative updates for pods an RS.
• StatefulSet. A workload API object that manages stateful applications, such as databases.
• DaemonSet. Ensures that all or some worker nodes run a copy of a pod. This is useful for daemon applications like Fluentd.
• Job. Creates one or more pods, runs a certain task(s) to completion, then deletes the pod(s).

Micro Service

A specific part of a previously monolithic application. A traditional micro-service based architecture would have multiple services making up one, or more, end products. Micro services are typically shared between applications and makes the task of Continuous Integration and Continuous Delivery easier to manage

Deployments

A way to define the desired state of pods or a replica set. Deployments are used to define HA policies to your containers by defining policies around how many of each container must be running at any one time.

Services

Coupling of a set of pods to a policy by which to access them. Services are used to expose containerised applications to origins from outside the cluster

Nodes

A (normally) Virtual host(s) on which containers/pods are run.

Kubernetes architecture and components

A K8S cluster is made of a master node, which exposes the API, schedules deployments, and generally manages the cluster. Multiple worker nodes can be responsible for container runtime, like Docker or rkt, along with an agent that communicates with the master.

Master components

These master components comprise a master node:

• Kube-apiserver. Exposes the API.
• Etcd. Key value stores all cluster data. (Can be run on the same server as a master node or on a dedicated cluster.)
• Kube-scheduler. Schedules new pods on worker nodes.
• Kube-controller-manager. Runs the controllers.
• Cloud-controller-manager. Talks to cloud providers

Node components

• Kubelet. Agent that ensures containers in a pod are running.
• Kube-proxy. Keeps network rules and perform forwarding.
• Container runtime. Runs containers.

Out of the box, K8S provides several key features that allow us to run immutable infrastructure. Containers can be killed, replaced, and self-heal automatically, and the new container gets access to those support volumes, secrets, configurations, etc., that make it function.

These key K8S features make your containerized application scale efficiently:

• Horizontal scaling.Scale your application as needed from command line or UI.
• Automated rollouts and rollbacks.Roll out changes that monitor the health of your application—ensuring all instances don’t fail or go down simultaneously. If something goes wrong, K8S automatically rolls back the change.
• Service discovery and load balancing.Containers get their own IP so you can put a set of containers behind a single DNS name for load balancing.
• Storage orchestration.Automatically mount local or public cloud or a network storage.
• Secret and configuration management: Create and update secrets and configs without rebuilding your image.
• Self-healing:The platform heals many problems: restarting failed containers, replacing and rescheduling containers as nodes die, killing containers that don’t respond to your user-defined health check, and waiting to advertise containers to clients until they’re ready.
• Batch execution: Manage your batch and Continuous Integration workloads and replace failed containers.
• Automatic bin-packing: Automatically schedules containers based on resource requirements and other constraints.

Docker Swarm overview

Docker Swarm is native to the Docker platform. Docker was developed to maintain application efficiency and availability in different runtime environments by deploying containerized application microservices across multiple clusters.

 Docker swarm , is a container orchestration tool native to Docker that enables applications to run seamlessly across multiple nodes that share the same containers. In essence, you use the Docker Swarm model to efficiently manage, deploy, and scale a cluster of nodes on Docker.

Differences between Kubernetes and Docker Swarm

Kubernetes and Docker Swarm are both effective solutions for:

• Massive scale application deployment
• Implementation
• Management

oth models break applications into containers, allowing for efficient automation of application management and scaling. Here is a general summary of their differences:

• Kubernetes focuses on open-source and modular orchestration, offering an efficient container orchestration solution for high-demand applications with complex configuration.
• Docker Swarm emphasizes ease of use, making it most suitable for simple applications that are quick to deploy and easy to manage.
• Kubernetes uses its own client, API and YAML definitions which each differ from that of the standard Docker equivalents. In other words, you cannot use Docker CLI nor Docker Compose to define containers. When switch platforms, commands and YAML definitions will need to be rewritten.
• The Swarm API provides much of the familiar functionality from Docker itself but does not fully encompass all of its commands. It supports many of the tools that work with Docker, however, if Docker API lacks a specific operation there is no easy way around it using Swarm.

Scalability

• Kubernetes is in comparison more of an all-in-one framework for distributed systems. Its complexity stems from offering a unified set of APIs and strong guarantees about the cluster state, which slows down container deployment and scaling.
• Docker Swarm is able to deploy containers faster than Kubernetes even in very large clusters and high cluster fill stages allowing fast reaction times to scaling on demand. New replicas can be started with a single update command.

High availability

• Kubernetes and Docker Swarm both ensure high availability of services through replication. The same container is deployed to multiple nodes to provide redundancy and redeployed again if a host running the service goes down making the services self-healing. While either of the container orchestrators can be run on a single server, additional nodes are required for true redundancy.

Data volumes

• Kubernetes volumes are an abstraction to allow containers to share data within the same pod. The volumes have an explicit lifetime, they are created and removed together with the pod they are enclosed in. Volumes work in basics just as any other directory, which is accessible to the containers in the same pod. Kubernetes also supports external data volume managers to transfer data between pods.
• Docker data volumes are directories shared within one or more containers. Volumes are created separately or together with containers and can be shared between multiple containers. Data volumes also persist even when containers using them are deleted. Volumes by themselves are however only local to the node they are created on. To create global volumes, Docker engine supports volume plugins.

Networking

• Kubernetes commonly uses flannel to accomplish container networking. Containers are joined in a virtual network and announced through etcd. TLS authentication is also possible but requires certificates to be generated and installed manually to all nodes.
• Docker Swarm forms a multi-host ingress network overlay that connects containers running on all cluster nodes. More inter-container networks can be configured manually. Connections between nodes are also automatically secured through TLS authentication with certificates.

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