Introduction to Container Orchestration
In a cloud-native world, containers dynamically transform the way application workloads are developed, deployed, and maintained. With containers, developers can efficiently deploy and manage low-latency, robust and reliable applications with OS-level abstraction. As essential enablers to deploying applications at scale, orchestration tools help automate all aspects of a container lifecycle efficiently.
In this article, we delve into Kubernetes and Docker Swarm as two of the most popular container orchestration tools, while learning their similarities and differences.
Initially developed by Google, Kubernetes is the most popular open-source container orchestration tool that was later contributed to the Cloud Native Computing Foundation (CNCF). Designed from scratch with orchestration in mind, Kubernetes follows a Client-Server model where a Master Node instructs Worker Nodes for process synchronization. By enabling a framework of distributed systems, Kubernetes aids faster development of applications that are scalable, portable, and resilient.
Key Points to Note
- Supported by CNCF and a huge community of developers and contributors
- Open-source, and not limited to a particular vendor/OS
- Easy to customize and organize processes through container PODs
- Superior load-balancing and fault tolerance
- Automated Rollout, Rollback, and Service Discovery
- Complex to set up and requires a steep learning curve
- Requires separate tools for provisioning containers
Built on top of the Docker Engine as a default clustering solution, Docker Swarm is used to manage the state of clusters and then reconcile them with a pre-defined desired state.
By integrating multiple Docker containers (nodes) onto a single cluster, a Swarm Manager controls the activities of each of these nodes to form a seamless environment for scaling and managing application workloads. As opposed to Kubernetes, using Docker Swarm does not require the installation of additional software since a cluster can be initiated directly from the Docker CLI.
Key Points to Note
- Requires short learning curve and is easy to configure
- Smoothly integrates with the Docker CLI
- Does not support auto-scaling
- Offers limited fault tolerance
- As opposed to Kubernetes, lacks contribution from a large community of developers
Comparing Kubernetes and Docker Swarm
While there are fundamental differences in how they operate, Docker Swarm and Kubernetes share a few features and functionalities. These include:
- Managing containerized workloads by defining a cluster’s desired state
- Creating clusters made up of single master and multiple worker nodes
- Extending multi-platform support
As two of the most used container orchestration tools, Docker Swarm and Kubernetes almost provide the same functionality. There are, however, certain basic differences in how they manage containers. These differences include:
Installation & Container Setup
- Docker Swarm comes with the standard installation of Docker Desktop. This means that developers and administrators mainly deal with one set of tools to set up and orchestrate containers. Once Docker is set up on a machine, initiating Docker Swarm is a two-step process:
<indent><indent> 1. Giving each node an IP address
<indent><indent> 2. Defining communication protocols and ports
<indent><indent> Application installations are managed through Docker Compose - a tool that uses specifications of a YAML file to configure application services. By leveraging the Docker Swarm API, containers in a Swarm are managed using most of the tools that already run with Docker. Though this offers similar functionality as the Docker Engine, Swarm is, however, not effective if Docker’s Application Programming Interface (API) lacks specific Docker operations.
- On the other hand, Kubernetes supports multiple installation options as it is platform-independent and can be hosted on a local machine, network drive, or the public cloud. However, this means that Kubernetes requires extensive manual installation, thereby following different configuration approaches for different platforms. To manage workloads using Kubernetes, the <terminal inline>kubectl<terminal inline> CLI utility needs to be installed on the user’s machine. Developers are also required to have knowledge of a cluster’s configuration and node functions before managing those in Kubernetes.
- In Kubernetes, an application is deployed using a combination of PODs, deployment objects, and services. Kubernetes relies on its own definition files for cluster objects, making Kubernetes objects substantially different from standard Docker equivalents. Kubernetes orchestrated containers cannot be configured using Docker Compose or the Docker Command-Line. This means that definition files have to be rewritten when moving an application from Docker Swarm to Kubernetes.
Networking & Service Discovery
- In Docker Swarm, when a node joins a cluster, it creates:
<indent><indent> 1. An Overlay Network covering all services within the Swarm
<indent><indent> 2. A Docker Bridge Network for all containers on the host
<indent><indent> Network traffic can then be encrypted when creating the overlay network for secure communication. Once applications are deployed on a cluster, an orchestrator breaks down services into multiple tasks. These tasks are then assigned IP addresses by an allocator, while a dispatcher allocates these to individual worker nodes.
- In Kubernetes, PODs communicate via a flat, peer-to-peer network that allows all PODs to communicate with each other. This network requires two Classless Inter-Domain Routers (CIDRs) per cluster:
<indent><indent>One for communication between services, and
<indent><indent>the other to assign IP addresses to the PODs.
<indent><indent>Network Policies can further be created to manage and restrict interactions between each POD. Additionally, Kubernetes allows containers to be exposed either via IP address or DNS names. This enables services to be easily discovered for efficient network distribution and load balancing.
- Kubernetes supports automated scaling through:
<indent><indent>Cluster Autoscaling at the cluster level
<indent><indent>Pod level autoscaling using Horizontal Autoscaling
<indent><indent>To scale an application, Kubernetes creates new PODs and schedules them on nodes with sufficient resources. Since Kubernetes is an all-inclusive network with highly distributed hosts, there are strong guarantees in terms of POD communication and cluster state. In addition to this, Kubernetes allows batch execution and CI workloads to replace and provision nodes for efficient scalability. With autoscaling, Kubernetes not only provisions newer PODs but also kills them when their purpose is met - however, this process is known to affect the cluster's operating efficiency.
- Docker Swarm, in contrast to Kubernetes, allows on-demand scaling without impacting cluster efficiency. The process essentially involves the Swarm Manager replicating the number of Worker Nodes to handle increasing traffic loads. With a single <terminal inline>docker update<terminal inline> command, Swarm allows faster replication even on complex cluster setups.
- In Kubernetes, instances of a POD are distributed among multiple nodes to make the application fault-tolerant, providing High Availability. Kubernetes also makes use of load balancing mechanisms to detect and evict unhealthy PODs, thereby supporting High Availability. By using <terminal inline>kubeadm<terminal inline> and a Multi-Master approach, Kubernetes maintains high-availability by provisioning the optimum number of <terminal inline>etcd<terminal inline> cluster nodes internally or externally within a control plane.
- On the other hand, Docker Swarm takes advantage of Swarm node replicated services to ensure high-availability. The Swarm manager is controlled through an out-of-the-box Internal Distributed State Store for managing an entire cluster state as well as worker node resources to maintain highly-available container instances.
Graphical User Interface
- Kubernetes comes with the default Kubernetes Dashboard that provides a web interface for users to manage and monitor cluster resources. The dashboard provides an easy-to-use interface that helps with:
<indent><indent>1. Critical insights of the current cluster state, including Jobs, Services, Deployments, and DaemonSets.
<indent><indent>2. Manage resource consumption
<indent><indent>3. View event logs and error information
<indent><indent>4. Create Kubernetes clusters for containerized applications
<indent><indent>Kubernetes also supports third-party visualization and graphical tools like Grafana that provide a richer, more customized interface for an enhanced user experience.
- As opposed to Kubernetes, Docker Swarm does not come with an in-built GUI. It, however, supports multiple third-party visualization tools such as Portainer, Dockstation, Swarmpit, etc. for users to effectively manage workloads and container orchestration.
- As PODs within Kubernetes are exposed through a ClusterIP service, these can be implemented as a cluster load balancer. Kubernetes uses an Ingress Element to balance workloads by routing traffic among multiple idle PODs.
- Docker Swarm uses a DNS Element that helps distribute incoming requests and group them by service name. These services can be configured on user-defined ports, ensuring proper assignment of workloads.
This article highlighted some of the fundamental similarities and differences between Kubernetes and Docker Swarm.
Both Kubernetes and Docker Swarm are excellent options for orchestrating containers. Kubernetes is, however, considered more suitable for larger production workloads that require a lot of tools and specialized expertise.
While both of these tools offer similar services, they follow a slightly different approach to container orchestration.
With their differences in mind, it is important to realize that both of them solve advanced challenges to make an organization’s workload efficient. By the end of it all, the goal remains the same - that is to maintain an efficient cloud-native system that enables faster development and deployment of applications that are scalable, secured, and resilient.
Sudip Sengupta is a TOGAF Certified Solutions Architect with more than 15 years of experience working for global majors such as CSC, Hewlett Packard Enterprise, and DXC Technology. Sudip now works as a full-time tech writer, focusing on Cloud, DevOps, SaaS, and Cybersecurity. When not writing or reading, he’s likely on the squash court or playing Chess.