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If you’ve been working with containers, chances are you are familiar with Kubernetes. Everyone seems to love the K8s platform. But the real question is will it continue being just as relevant five or ten years into the future? In this article, we understand what Kubernetes is, how it compares to Dockers, and what the future of Kubernetes would look like.
Also known as K8s or Kube, Kubernetes is an open-source system for deploying, scaling, and managing containerized applications. It automated the operations of Linux containers and eliminated many of the manual processes involved in deploying and scaling centralized applications.
But wait, what are containers? Containers are executable units of software wherein the application code is packaged (along with libraries and dependencies) to run anywhere on the desktop, in traditional IT, or in the cloud. Containers are designed to be ephemeral in that a container can crash or die without losing user data because the data is stored outside the container.
The engineers at Google developed Kubernetes as an open-source project in 2014. Based on Borg, the container orchestration platform used by Google internally. The future of Kubernetes coherently depends on its capabilities in the present day.
Kubernetes gives you a platform that you can use to schedule and run containers on clusters of virtual or physical machines. That is to say, it assists you in fully implementing a container-based infrastructure in production environments. Developers can also use Kubernetes patterns to develop applications that have cloud-native features with Kubernetes as a runtime platform.
Kubernetes allows you to:
Before we compare and ponder the future of Kubernetes and Docker, let us briefly understand what Docker is.
Docker is a commercial containerization platform and runtime that assists developers in building, deploying, and running containers using a client-server architecture with simple commands and automation through a single API. It is a suite of tools developers can leverage to build, run, share, and orchestrate containerized applications.
The Docker container architecture looks something like this:
Developer tools for building container images: Through Docker Build and Docker Compose. The former creates a container image, i.e., the blueprint for a container. This includes everything needed to run applications—from app code, binaries, and scripts to dependencies, configurations, and environment variables. Docker Compose is a tool that defines and runs multi-container applications.
Sharing images: Docker provides a registry service called Docker Hub for sharing container images.
Running containers: Container runtime such as Docker Engine run in almost any environment: Linux and Windows servers, Mac and Windows PCs, the cloud, edge devices, etc.
Built-in container orchestration: Managing a cluster of Docker Engines called swarm is done by Docker Swarm.
Docker is a software development tool suite that allows developers to create, shape, and run individual containers. Kubernetes can be defined as a system for operating containerized applications. Think of containers as standardized packaging for microservices with all needed application code and dependencies inside. Docker is responsible for creating these containers. Now, a modern application consists of many containers. Operating these containers in production is Kubernetes’ job.
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Docker and Kubernetes are mostly complementary technologies. While Docker offers an open standard for distributing and packaging containerized applications, Kubernetes enables the orchestration and management of all container resources from a single control plane. Using Kubernetes with Docker makes your infrastructure more robust and your application more scalable and highly available. This combination is recommended regardless of the discussion on the future of Kubernetes.
Keep up with the most stable: Even though the latest version of Kubernetes may have unfamiliar features or limited support, it may be incompatible with your existing setup. Still, the rule of thumb is always to keep your K8s updated to the latest stable version. This version will most likely come with all security or performance issues patched. You will also be able to find community or vendor support more easily and avoid security, performance, or cost anomalies.
Be friends with Versioning Config Files: Store all config files, like deployment, services, ingress, etc., in a version control system before pushing your code to a cluster. This will allow you to track source code changes and quickly undo the change and restore your cluster to stability and security whenever needed.
Declarative YAML Files: Replace imperative kubectl commands with declarative YAML files. These can be added to your cluster with the kubectl apply command. Kubernetes helps to figure out your desired state while adopting a declarative approach. With YAML files, you can store and version all your objects along with your code and roll back deployments in case of errors. Moreover, this system lets your team see the cluster’s current status and changes.
Lint your Manifests: If you find YAML tricky, you can use yamlint to support multi-documents within a single file. Kubernetes-specific linters are also available with Kube-score, where you lint your manifests and maintain best practices, and kubeval, which lints your manifests and checks the validity. The 1.13 version of Kubernetes has a dry-run option on kubectl that allows the system to inspect your manifests without applying them. You can use this feature to verify that your YAML files are valid for K8s.
Adopt a Git Workflow: A Git-based workflow is a great automating model for all tasks using Git as the single source of truth. Adopting GitOps improves productivity, speeds up deployments, improves error tracking, and automates your CI/CD processes.
Couple Pods to Deployments, ReplicaSets, and Jobs: Nake pods are not bound to a Deployment or ReplicaSet, and it is better to avoid them. It is impossible to reschedule them in case of a mode failure. On the other hand, a deployment helps maintain a desired number of pods by reading a ReplicaSet and defining a strategy for replacing pods.
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Clear Labelling of your K8s Resources: Each K8s cluster has different components like containers, services, networks, and pods. Keeping track of all these resources can get difficult with the cluster’s growth. Labels are attributed to a particular resource in Kubernetes clusters, making it easy to filter and select objects with kubectl. Therefore, it is recommended that you use as many descriptive labels as you can to differentiate between the resources. Labeling can be done by version, owner, component, instance, project, team, confidentiality level, etc.
Simplify resource management with Namespaces: Namespaces allow your team to logically divide a cluster into sub-clusters. This proves to be particularly helpful in sharing Kubernetes clusters with different projects/teams simultaneously. With Namespaces, different teams can work together in the same cluster without interfering with each other’s projects. K8s provides 3 namespaces: default, kube0system, and Kube-public.
Run liveness Probes: Liveness probes are responsible for performing regular health checks on pods and preventing K8s from directing traffic to unhealthy ones. K8s automatically restarts the pods that fail a health check, thus ensuring the availability of your application. The probe notifies the pod of a response, and the absence indicates that your app is not running on that specific pod. This triggers the probe to release a new pod and run the app there. However, an important thing to remember is to run a start-up probe first. The liveness and readiness probes will not target pods if their start-up probe is incomplete.
Use custom readiness probes: A readiness probe helps track the health of your Kubernetes apps. It assesses a pod’s capability to accept traffic. On detecting an unresponsive pod, it triggers the pod to restart. Here, it is important to configure a time delay to allow large config files to load. Otherwise, the probe may terminate the pod before it loads fully, creating a restart loop.
Detect and respond to SIGTERM: When attempting to safely stop a container, K8s will send the SIGTERM signal. You’ll want to respond to this signal as necessary in your app. Remember to configure terminationGracePeriodSeconds on your pods to stop containers with a grace period. The default duration of this command is 30 seconds, but your app may require more or less time, so set it up accordingly.
Use resource requests and caps: The minimum amount of resources a container can consume is defined by the user resource requests. Resource limits define the maximum consumption. Without resource requests and limits, production clusters can fail if resources are insufficient. Pods in a cluster may also consume excess resources leading to increased K8s costs. Furthermore, too much consumption of CPU or memory can cause nodes to crash.
Role-based access controls: These allow you to define which users can access Kubernetes resources, the clusters they can access, who can make the changes, and the extent to which they can make them. Role-based permissions can be set in two ways:
Keep apps stateless: Stateless applications are generally easier to manage. A stateless backend ensures that new teams to K8s don’t have long-running connections that limit scalability. Migrations and on-demand scaling are also easier with stateless apps. In addition, they allow you to take advantage of spot instances.
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Firewall your K8s Environment: This is K8s security-best practice that limits external requests to the cluster from reaching the API server. A firewall can be set up by using regular or port firewall rules. Some other security-best practices that roots for the future of Kubernetes include:
Establish network policies: Network policies in K8s specify which traffic you will allow irrespective of how traffic moves between pods; it will only be allowed if the network policies allow it. To create a network policy, it is important to define authorization.
Choose smaller images: The smaller the image, the faster the build and the less storage you need. The efficient layering of an image helps reduce its size. Images can also be optimized by building them from scratch. In case you need many different components, you can use multiple FROM statements in a single Dockerfile. This setup will pull each layer based on the command within the deployed container. The feature will generate sections—each of which refers to a distinct base image. The resulting Docker container will be slimmer because it only contains the components you need, not the previous layers.
Alpine images over base images: Alpine images can be 10 times smaller than base images. This takes up less space, speeds up builds, and pulls images faster.
Choosing and activating the right auto-scaler: Kubernetes gives you the advantage of quickly scaling resources up or down depending upon your requirements. However, choosing the right auto-scaler is crucial to realizing this. Kubernetes offers 3 options:
Stderr and Stdout Logs: Stdout is the standard output stream where you send application logs, and stderr is the standard error stream where you send error logs. A container engine stores the file in JSON format whenever an app writes to either. Containers, pods, and nodes in K8s are highly dynamic and require consistent and persistent logging. Therefore, it is recommended that you keep cluster-wide logs on different backend storage.
Budget pod disruption: Draining a node deletes and reschedules all pods. However, if you have a heavy load and cannot afford to lose over 50% of your pods, defining a Pod Disruption Budget allows you to protect the Deployments from unexpected events.
Track your K8s control plane: Monitoring the control plane helps identify vulnerabilities within the cluster. K8s provides robust and automated monitoring tools that optimize performance and reduce costs.
Adopt K8s storage best practices: Here are some K8s storage best practices that you should use, especially while creating a life cycle for Persistent Volume (PV):
With Kubernetes, teams can add more stability and management to their operations. It scales easily, has no vendor lock-in, and saves enterprises’ operational costs. However, a few reasons indicate that this well-loved open-source container orchestration platform’s future is bleak.
First, Kubernetes is hard. It is complex and significantly different from almost every other type of platform. Moreover, the fact the K8s developers frequently roll out new features makes it all the more difficult to master the platform. Some experts also believe that Kubernetes currently lacks real usability tooling. The K8s dashboards support only a subset of all K8s functionality and lack user-friendliness.
Next, the native functionality of K8s is pretty limited. The core function of K8s is to orchestrate applications. You need add-ons or plug-ins to access scalable monitoring, complex network configurations, storage management, etc.
Finally, not all workloads can run on Kubernetes. Natively, it can only run containers. When compared to other hosting platforms, this puts K8s on the downside. However, despite these challenges, the disappearance of Kubernetes seems unlikely. The future of Kubernetes is expected to follow a steady graph.
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