Kubernetes has undeniably earned a place in the contemporary technological environment as a dominant platform that propels businesses to manage and deploy applications on an impressive scale. This stalwart of the container orchestration sphere simplifies the provisioning of infrastructures for applications based on microservices, promoting efficiency through modular workload management. Here we will explore Kubernetes deployment strategies that lead to end-to-end transformation.
Kubernetes presents an array of deployment resources that facilitate the execution of CI/CD pipelines, leveraging updates and version control. Though the default strategy involves rolling updates, Kubernetes has the versatility to support unconventional deployment or updating approaches to cater to unique requirements for cluster services.
This composition dives deep into the ocean of Kubernetes deployment concepts, outlining various sophisticated Kubernetes deployment strategies, their strengths, drawbacks, and potential applications.
Unraveling the concepts of Kubernetes deployment
In the Kubernetes realm, deployments serve as resources that enable the declarative updating of applications. By employing these deployments, cluster administrators can blueprint an application’s lifecycle, outlining how updates tied to it should be enacted.
Kubernetes deployments form an automated mechanism to realize and sustain the desired state for cluster objects and applications. The Kubernetes machinery oversees the deployment process autonomously, assuring a secure and reproducible methodology for executing application updates.
Kubernetes deployments equip cluster administrators to:
- Unleash a pod or replica set
- Update replica sets and pods
- Revert to previous versions
- Halt or resume deployments
- Scale deployments
The subsequent segment illuminates how Kubernetes eases the update process for containerized applications and addresses the obstacles of continuous delivery.
Exploring Kubernetes objects
Kubernetes employs a multitude of workload resource objects, durable entities that govern the cluster state. The Kubernetes API exploits Deployment, ReplicaSet, StatefulSet, and DaemonSet resources for making declarative amendments to an application.
Deployment In the Kubernetes realm, a deployment acts as a resource to outline and recognize the desired state of an application. The cluster administrator illustrates the anticipated state in the YAML file of the deployment, which is then used by the deployment controller to methodically alter the actual state to match the desired one. The deployment controller also incessantly supervises, and swaps failed cluster nodes and pods with their healthy counterparts to assure high availability.
ReplicaSet
A ReplicaSet serves to sustain a certain count of pods, guaranteeing high availability. The manifest file of the ReplicaSet contains:
- A selector to recognize pods that form part of the set
- The count of replicas to indicate the number of pods that should be in the set
- A pod template to depict what data the fresh pods should generate to fulfill the criteria of the ReplicaSet
StatefulSet
A StatefulSet object governs the deployment and scaling of pods within a stateful application. It supervises the pods based on identical container specifications, assuring appropriate sequencing and uniqueness for a set of pods. The persistent identifiers of the StatefulSet’s pods enable administrators to tether their workloads to persistent storage volumes, ensuring constant availability.
DaemonSet
DaemonSets facilitate the maintenance of application deployments, ensuring that a pod copy runs on a group of nodes. A DaemonSet resource primarily manages the deployment and lifecycle of various agents like:
- Cluster storage agents on each node
- Log collection daemons
- Node monitoring daemons
- You can find more details on the diverse Kubernetes workload resources here
Kubernetes deployment updates
Kubernetes Deployments provide a predictable route to launch and halt pods. These resources streamline the deployment, rollback of changes, and management of the software release cycle in a self-reliant and iterative fashion. Kubernetes extends various deployment strategies, enabling smaller and more frequent updates, yielding benefits like:
- Quick customer feedback for better feature enhancement
- Decreased time to market
- Boosted productivity in DevOps teams
While the default strategy is rolling updates, Kubernetes also backs advanced deployment strategies like blue-green, canary, and A/B deployments, contingent on the goal and feature types.
We will now delve into these strategies in more detail.
Advanced strategies for Kubernetes deployment
Kubernetes provides multiple paths to release application updates and features based on the workload and use case involved. In live production settings, it’s essential to employ deployment configurations hand in hand with routing features so that updates influence specific versions. This allows release teams to test the effectiveness of updated features in live environments before they fully commit to the versions. Kubernetes backs advanced deployment strategies to give developers precise control over traffic flow toward specific versions.
As we survey the contemporary technological arena, Kubernetes surfaces as a popular platform that furnishes businesses with the capabilities to scale and govern applications effortlessly. This container orchestration mechanism decomplexifies infrastructure provisioning for microservices-oriented applications, thereby endorsing efficient management of workloads via a modular approach.
Kubernetes offers a multitude of deployment resources for enabling the streamlined implementation of Continuous Integration/Continuous Deployment (CI/CD) pipelines using frequent updates and version control. Although Kubernetes proffers rolling updates as a standard deployment method, a variety of scenarios necessitate a divergent and more strategic approach towards deploying or updating services within the cluster.
Blue-Green Deployment
The blue-green strategy involves deploying both the old and new instances of the application simultaneously. Users have access to the existing version (blue), while the new version (green) is accessible to Site Reliability Engineering (SRE) and QA teams with an equal number of instances. Once the green version passes all the testing requirements, users are redirected to the new version. This is achieved by updating the version label in the selector field of the load balancing service.
Blue-green deployment is predominantly applicable when developers wish to circumvent versioning issues.
Blue-Green deployment strategy:
Pointing version 1.0.0:
Pointing version 2.0.0:
Canary Deployment
In the canary strategy, a subset of users is directed to the pods hosting the new version. This subset is progressively increased while the users connected to the old version are gradually decreased. This strategy involves comparing the subsets of users linked to both versions. If no bugs are detected, the new version is fully rolled out to all users.
Using Canary deployment strategy
Step 1: Deploy the needed no. of replicas by:
Deploying the first application
Step 2: Deploy version 2 instance
Step 3: Check whether the second version was successfully deployed
Step 4: Once the deployment is successful, increase the no. of instances of version 2:
Step 5: Once all replicas are up, delete version 1:
A/B Deployment
With A/B deployments, administrators can route a specific subset of users to a newer version under certain conditions or limitations. These deployments are primarily performed to evaluate the response of the user base to certain features. A/B deployment is also referred to as a “dark launch” as users are kept uninformed about the inclusion of newer features during testing.
A/B strategy is deployed using Istio service mesh:
Step 1: Deploy both versions of the application
Step 2: The versions are exposed using Istio gateway to match requests to the first service using:
Step 3: The Istio VirtualService rule can then be applied:
This distributes the traffic among versions on a 1:10 ratio. To shift the traffic inflow, the weight of each service is edited. Then, VirtualService rule is updated via Kubernetes CLI.
When to use each advanced deployment strategy?
Since Kubernetes use cases diverge based on availability requirements, budget constraints, available resources, and other factors, there is no universal deployment strategy.
Summary
Kubernetes objects form the cornerstone of the platform’s functionality, facilitating rapid delivery of application updates and features. With deployment resources, Kubernetes administrators can establish an efficient versioning system to manage releases while ensuring minimal to zero application downtime.
Deployments allow administrators to update pods, revert to previous versions, or scale-up infrastructure to cater to escalating workloads.
The advanced Kubernetes deployment strategies explored herein also enable administrators to direct traffic and requests toward specific versions, allowing for live testing and error detection. These strategies ensure that newer features perform as intended before the administrators and developers fully commit to the changes.
While deployment resources lay the foundation for persisting application state, it is always recommended to judiciously select the right deployment strategy, prepare comprehensive rollback options, and consider the dynamic nature of the ecosystem that relies on multiple loosely coupled services.
