Kubernetes, popularly known as k8s, is a container orchestration tool designed for cloud native applications. Kubernetes automates the deployment, scaling and overall management of the containerized applications across all Kubernetes clusters. Kubernetes Deployments are self healing, utilize declarative configuration, support rolling updates and rollbacks, and can scale automatically.

With the rise of loosely coupled, distributed microservices, applications are now packaged into containers. A containerized application consists of all of its code and libraries packaged together in what is called a container. Kubernetes is the open source tool that facilitates the deployment and management of these cloud-native applications in any isolated environment.

Before delving into Kubernetes deployment, let us understand a few important terms about the deployment process on a kubernetes cluster.

Once the development team writes the application source code, they package the code, dependencies, and libraries into an immutable snapshot called as the container image. This image serves as a blueprint for running containerized applications.

Container images are stored in a container registry like Docker Hub, which is a centralized repository to access and manage container images.

kubectl is the command line tool used to interact with the Kubernetes cluster. In later sections of this article, we will demonstrate how to use kubectl commands to deploy, manage, monitor, and debug cluster resources.

Kubernetes uses YAML files for deployment of container images into its clusters. These files consist of all the application configuration details required to deploy applications into the Kubernetes cluster and manage them within the cluster.

A Kubernetes cluster is a set of nodes that run containerized cloud-native applications, managed by Kubernetes. It consists of at least one control plane node and one or more compute (worker) nodes.

Container runtime is the engine that runs the containerized applications inside a node. Some popular container runtimes are Docker and CRI-O.

The cloud applications are deployed inside pods, present in the worker nodes. Containers within the same pod share the same IP address and network namespace so that they can communicate with each other and share resources.

A quick overview of the deployment steps is as follows:

The smallest unit in the Kubernetes architecture is the pod, which represents a single instance of a running process in a Kubernetes cluster. Pods consist of one or more containers.

At the outset, we would think that whenever a new pod creation request comes in, the kube-apiserver writes it to etcd and then etcd sends a response saying that it's done. However, a lot goes on between the various components to create, modify, or delete a pod once 

The diagram below shows some of the important components that make up the Kubernetes architecture. The control node consists of components like kube-apiserver, etcd, and the scheduler. The kube-apiserver receives and processes the incoming request and sends it to the compute node for execution. The key-value datastore, etcd, is distributed across the cluster.

Upon receiving a request, the kube-apiserver writes the desired state to etcd. The scheduler then checks for the available nodes where the Pod can be placed based on resource availability and constraints. Note that each node has a number of pods, and each pod can have one or more containers grouped together. The scheduler informs the kube-apiserver of its decision, and the kube-apiserver then instructs the kubelet on the chosen worker node to create the pod. The kubelet is an agent present on each worker node and is responsible for managing the state of pods on its node. For example, the kubelet is responsible for taking actions to ensure that the desired state of the pod matches the actual state.

There are several Kubernetes deployment strategies, which are discussed in brief below.

: This is the default deployment strategy in Kubernetes. Old pods are gradually replaced with new ones. You can configure the update behavior using parameters like maxUnavailable (the maximum number of pods that can be unavailable during the update) and maxSurge (the maximum number of extra pods that can be created during the update) to balance availability and minimize disruption during updates.

: In this approach all the existing pods are terminated before creating a new set of pods. It is faster than the default strategy (rolling update), however, it causes downtime and is suitable for stateless applications since they do not retain persistent state between restarts.

: In Kubernetes, you can use two separate deployments, with blue representing the current version and green representing the new version, and a service to switch traffic from the old version to the new version once it's ready and stable. This can be managed manually or automated using tools like ArgoCD. This type of deployment has zero downtime and is ideal for applications that require high availability.

: In a canary deployment, the new version of an application is released to a small group of users first (the canary), before rolling it out to everyone. If the new version works well for the small group, it is then gradually rolled out to all users. This strategy reduces the risk of any issues impacting all users, ensuring a smoother and safer deployment process.

: This strategy involves directing different users to different versions of your application, allowing you to test multiple versions simultaneously. By splitting users into groups, you can compare how each version performs and choose the best one. This approach helps in fine-tuning features and improving user experience.

Kubernetes also offers tools to optimize resource usage within a deployment, such as the maximum resources a pod can utilize, minimum resources required to perform a stable operation, horizontal and vertical automatic pod scaling, and automatic scaling of cluster nodes, thereby optimizing cloud resource usage.

Kubernetes deployment is simple and can be performed in just a few steps, with the right configurations.

All you need is a Kubernetes cluster that you can set up using any private or public cloud infrastructure, on-premises development environment, or managed Kubernetes service.

, a tool that provides minimal setup, which is ideal for testing purposes.

To set up a production cluster on a cloud environment without a managed service, you can download and install 

 (command line tool to interact with the cluster).

If you want to use a managed service, you can use the Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), and many others depending on the cloud provider you choose. Managed services provide automatic provisioning and setup of clusters.

Once you have configured kubectl to connect to your Kubernetes cluster, you need to define the deployment spec that describes the desired state of your application.

Defining the deployment spec involves creating a YAML or JSON file with the necessary configuration parameters. Here is an example YAML deployment file for a database service:

The above YAML, when executed, creates a new pod, as per the deployment strategy specified. You can use:

Let us briefly understand the different fields of the YAML. The “kind” indicates the type of Kubernetes resource. In the above YAML, the kind is StatefulSet, which is used to manage stateful applications, like a database. A StatefulSet is used for applications that require stable, unique network identities and persistent storage across pod restarts. The other kind is “deployment”, which manages stateless applications.

The metadata identifies and describes the resource, such as its name and additional information about the pod.

The next part is the specification, which describes the service to be deployed, the number of replicas to be created, and other important information about the desired state of the application.

Replica set ensures that the specified number of replicas of the pod are up and running at all times.

A selector is a filter that chooses the pods corresponding to the given label. By using matchLabels, you can directly select the object based on the label and match with the Kubernetes resource. This ensures that any traffic is redirected only to the pods that match the label. We can have different labels for frontend, backend, db, production, testing, master, worker, and so on. Selector will select only those pods that match the criteria, like in the above case, 

, selects only those resources that have app value as db. In the same way, you can define environment: production, or nodes: master to select only those pods.

The template tag contains the pod template. The template tags define the desired pod configuration used by Kubernetes to create and manage pods. You define the containers the pod contains, the container image, and the port. Since this example is of a StatefulSet, we also mention the volumeMounts. This tag is used to mount volumes (making storage space available) for persistent data storage so that containers can access the data. The volumeClaimTemplates ensures that each pod has its own persistent storage and allows for dynamic provisioning of persistent volumes.

The deployment strategy is defined under the strategy field of the YAML file.

Nginx deployment has great web server capabilities and is optimized for serving static content. It can easily manage a large number of concurrent requests and has a built-in reverse proxy for efficient load balancing.

A typical NGINX deployment in Kubernetes involves creating a deployment configuration that defines the desired state using the NGINX spec. As given below, the YAML specification includes details such as the NGINX image version, which determines the version of NGINX to be deployed, and the number of replicas (set to create three NGINX pods). These NGINX pods run in the Kubernetes cluster, serving web content and handling traffic efficiently based on the configuration specified in the NGINX spec. The below setup ensures that all three NGINX pods are consistently managed and scaled according to the defined NGINX image and deployment strategy.

To update a deployment, modify the deployment YAML and use apply on the updated file:

Verify the changes by checking the pod details:

You can also directly change a field value using the set command:

You can read more about specific rollback scenarios from 

Scaling is done to meet the workload demands by adding or removing pods. You can scale a deployment by using the scale command and change the number of replicas:

Kubernetes supports automatic scaling using horizontal pod autoscaler (HPA), which adjusts the number of pod replicas based on the demand. You can specify the maximum and minimum number of replicas in the YAML or while creating the HPA resource:

Kubernetes deployment - Rich text1

FAQs

Kubernetes deployment - Section Overview - FAQ

Kubernetes deployment is a resource object in Kubernetes that provides declarative updates to applications, ensuring consistency and reliability.

The main Kubernetes deployment strategies are recreate, rolling update, blue-green deployment, and canary deployment.

The primary purpose of a Kubernetes deployment is to manage application lifecycle, including creation, updates, scaling, and rollback of versions.

Kubernetes can be deployed on various environments, including on-premises data centers, public and private clouds, and hybrid cloud setups.

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Kubernetes deployment - Accordion

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Kubernetes deployment

FAQs

What is Kubernetes deployment?

What are the different Kubernetes deployment strategies?

What is the primary purpose of a Kubernetes deployment?

Where do you deploy Kubernetes?

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