Containers orchestration: Kubernetes vs Docker swarm

When deploying applications at scale, you need to plan all your architecture components with current and future strategies in mind. Container orchestration tools help achieve this by automating the management of application microservices across all clusters. 

There are few major containers orchestration tools listed below:

• Docker Swarm
• Kubernetes
• OpenShift
• Hashicorp Nomad
• Mesos

Today we'll talk about Docker Swarm and Kubernetes and we'll compare them in terms of features.

What is container orchestration 

Container orchestration is a set of practices for managing the Docker Containers at large scale. As soon as containerized applications scale to large number of containers, then there is need of container management capabilities. Such as provisioning containers, scaling up and scaling down, manage networking, load balancing ,security and others.  

Let's talk Kubernetes

Kubernetes is an open source, cloud native infrastructure tool that automates scaling, deployment and management of containerized applications. 

Kubernetes was originally developed by google and later was handed over to Cloud Native Computing Foundation(CNCF) for enhancement and maintenance. Kubernetes is the most popular and highly in demand  orchestrator tool. Kubernetes is complex tool and a bit difficult to learn compare to swarm.

Here are few main architecture components of Kubernetes below:

Cluster 

A collection of multiple nodes, typically at least one master node and several worker nodes(also known as minions)

Node

A physical or Virtual Machine(VM)

Control Plane

A component that schedule and deploys application instances across all nodes

Kubelete

An agent process running on nodes. It is responsible of managing the state of each nodes and it can perform several actions to maintain a desired state.

Pods

Pods are basic scheduling unit. Pods consist of one or more containers co-located on a host machine and share same resources.

Deployments, Replicas and ReplicaSets

Docker Swarm

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

A mix of docker-compose, swarm, overlay network can be used to manage cluster of docker containers.

Docker swarm is still maturing in terms of functionalities when compare to other open source container orchestration tools.

Here are few main architecture components of Docker swarm below:

Swarm 

A collection of nodes that include at-least one manager and several worker nodes.

Service

A task that agent nodes or managers are required to perform on the swarm.

Manager node

A node tasked with delivering work. It manages and distributes the task among worker nodes.

Worker node

A node responsible for running tasks distributed by the swarm's manager node.

Tasks

Set of commands

Choosing the right Orchestrator for your containers

Kubernetes focuses on open-source and modular orchestration, offering an efficient container orchestration solution for high demand applications with complex configuration.

Docker swarm emphasises ease of use, making it most suitable for simple applications that are quick to deploy and easy to manage.

Some fundamental differences between both 

GUI:

Kubernetes features an easy web user interface(dashboards) that helps you

• Deploy containerized application on cluster 
• Manage cluster resources 
• View an error log, deployments, jobs

Unlike Kubernetes, Docker swarm does not come with Web UI to deploy applications and orchestrate containers. But there are some third party tools which can achieve this with Docker.

Availability:

Kubernetes ensure high availability by creating clusters to eliminate ingle point of failures. You can use Stacked Control Plane nodes that ensure availability by co-locating etcd objects with all available nodes of a cluster during failover. Or you can use external etcd objects for load balancing while controlling the control plane nodes separately.  

For Docker to maintain high-availability, Docker uses service replication at swarm nodes level. A swarm manager deploys multiple instances of the same container with replicas of services in each.

Scalability:

Kubernetes supports autoscaling on both  cluster level and pod level. Whereas Docker Swarm deploys containers quickly. This gives the orchestration tool faster reaction times that allow for on-demand scaling.

Monitoring: 

Kubernetes offers multiple native logging and monitoring solutions for deployed services within a cluster. Also Kubernetes supports third-party integration to help with event-based monitoring.

On the other side Docker Swarm doesn't offer monitoring solution like Kubernetes. As a result you need to rely on third party applications to support monitoring. So monitoring a Docker Swarm is considered to e more complex than Kubernetes. 

 

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How and why container monitoring is so important

What is container monitoring?

Containers are ephemeral in nature, they are difficult to monitor compared to bare metal server based applications or even those running on virtualized server. Monitoring is critical to ensure avalability, performance and security of containers. So containers infrastructure requires new monitoring tools and strategies.

Container observability

Visibility and monitoring are essential a running environment and to optimize resource usage and costs.

Because each container image can have a large number of running instances and due to high pace at which new images and versions are introduced, problems can be easily spread across containers and applications and can interrupt the entire architecture. So this makes it very critical to identify the root cause of a problem as soon as it occurs.

In large scale containerized environments, this is only possible through dedicated cloud native monitoring tools.

But if you are unable to achieve observability so this can result in below:

• It is very difficult for developers and operations task to understand what is running and how it is performing. So without observability it is very difficult to troubleshoot the problem and meeting the SLA for a production system.
• Scalability is also the major challenge to achieve without observability. Scaling your application on demand can enhance your user's experience. But if scalability is too slow it can make it poor.

Challenges with container monitoring 

There are few challenges in container monitoring:

• Containers are ephemeral so provisioning and destroying a container very quick process. This is one of the biggest advantage but for complex and big production system it makes very difficult to identify the issue.
• Containers share resources. These consume resources from host machine. If there is no monitoring of resources on host machine then any point of time high CPU or memory spike can scare you and can lead your production running application to stop.

Then how can we monitor containers

You can always use alerting system to monitor your containers. Setting up alert across the delivery pipeline can prevent the risk of system failure at early stage.

What are the common features in monitoring tools 

• Real time monitoring 
• Performance baseline
• Anomaly detection
• Network Performance monitoring 
• Config monitoring 
• Dashboards
• API monitoring
• Alerting
• Automation

Here are famous container monitoring tools used by modern industries

Prometheus

Prometheus is open-source systems monitoring and alerting toolkit and it was originally built at SoundCloud. Prometheus collects and stores it's metrics s time series data ie. metrics information was stored with the timestamp at which it was recorded alongside optional key value pairs called labels.

features:

• A multi-dimensional data model with time series data identified by metric name and key/value pairs
• PromQL is a flexible query language to query the dimensionality 
• Multiple modes of  graphing and dashboard support

Grafana

With Grafana you can visualise, analyse and alert on your system. No matter where your data is stored you can create dashboards and monitor. your data source can be anything like postgres, mysql, redis etc. 

Apart from above two there are few more popular tools like ElasticsSearch and Kibana, Zabbix, datadog etc.

Note: If you think this helped you and you want to learn more stuff on devops, then I would recommend joining the Kodecloud devops course and go for the complete certification path by clicking this link