Exploring Advanced Deployment Strategies In Kubernetes

Kubernetes, popular as K8s, is best known for its open-source automated deployment system, scaling, and most importantly for the management of centralized applications. It usually functions by grouping up the containers that primarily shape an application into logical units for seamless management and discovery.

In this contemporary tech era, Kubernetes deployment is a vastly encouraged platform because it is portable, extensible, and works as an open-source platform to manage centralized tasks. Automating deployment, scaling, and software management, makes the handling of software applications smoother and a literal breeze.  

Instead of the fact that it offers some features identical to PaaS systems, it’s still worth mentioning that Kubernetes is not a traditional all-inclusive PaaS System. Where PaaS normally provides a thorough platform with preconfigured tools and services, Kubernetes operates more like a flexible orchestration tool. 

Being an open-source software, it promotes declarative configuration and automation at the same time and has a huge, ever-expanding ecosystem. 

What Is A Kubernetes Deployment Strategy?

At its crux, the Kubernetes Deployment strategy suggests how to create, refresh, upgrade, or even downgrade multiple versions of applications while triggering minimum disruption or downtime of the services.

K8s deployment is generally a strategy for not just deploying but also managing the applications within the Kubernetes cluster. Therefore, one of the key pros of the Kubernetes deployment strategy is that it alleviates the risk of interruptions and downtime of the services.  

As far as the challenges are concerned, fastening up the number of deployments when developing a cloud-native application is quite an uphill battle in K8s deployment strategies.

Role Of Kubernetes Deployments

Application deployment in production environments demands a deep consideration of multiple factors such as various upgrades, limitations, and minimizing the risks. 

In Kubernetes, various deployment strategies are available to address these concerns, including rolling updates, blue-green deployments, and canary deployments. 

This article will delve into these strategies and explore how they can be implemented using native Kubernetes objects or with the assistance of tools like Flagger.

Benefits Of Using Kubernetes Deployment

Updating containerized apps manually takes loads of time and energy and can be quite boring as well. However, in the other case, deployments are thoroughly handled by the Kubernetes backend, and the update stages are completed on the server side without direct involvement.

Using  K8s deployments enables organizations to multiply resource usage, minimize downtime, and boost the functionality of their applications. Its self-healing features and automatic failover systems favor continuous availability for applications, even in the case of failures and disruptions.

Other ​​Kubernetes deployment benefits are:

1. Container Orchestration

To attain the desired state of the application, deploying to Kubernetes takes care of the planning and slotting of the containers across various clusters of machines. 

2. Scalability

Deploying Kubernetes enables applications to expand horizontally by automatically adjusting the number of containers and adding or removing them according to resource usage and rules set by the user.

3. Service Discovery

The built-in service discovery mechanism is just another thing to talk about in the Kubernetes deployment strategy. This mechanism enables the containers to seamlessly interact with each other without any hurdle using stable network addresses. 

4. Load Balancing

Kubernetes is an open-source container orchestration stage that delegates the influx of network traffic to the containers through load balancing.

5. Storage Orchestration

Kubernetes offers a vast storage framework to entertain a dramatic storage outflow. Also, it connects storage volumes to suitable containers.

Kubernetes Deployment Strategies And Their Examples

Catering to diverse needs and scenarios, these strategies offer multiple ways to update, test, and manage application deployments in Kubernetes environments.

1. Rolling Update

The rolling update strategy primarily comes into action when an app is being upgraded by rebuilding an image. There may arise a few scenarios where an alternative approach could help but those are the rare cases. An alternative approach usually helps when resources are limited to a single node capable of running only one pod.  

In Kubernest, Rolling updates work as a default strategy. While gradually updating the pods simultaneously, it replaces the existing pods with the latest ones. The whole process is carried out without any cluster or downtime. 

Before starting to scale down pods with the older version, the rolling update utilizes a readiness probe to determine whether a new pod is ready or not. If any significant issue is detected, you can halt an update and roll it back without stopping the entire cluster. 

To execute a rolling update, simply modify the image of your pods with the Kubectl set image. This will automatically start a rolling update.

Other Features Of Rolling Updates

• The best part of this default configuration is that it eventually substitutes the old pods with new ones. 
• Rolling update drastically reduces the downtime. Also, it ensures that one replica at a time is working on the older version where the latest version is being deployed. 
• Being pretty helpful for various situations, this strategy also balances stability with speed. 

Let’s Understand With An Example

Suppose you have a web application solution with almost 10 replicas, now all you have to do is apply the newest changes to the application. With a rolling update, Kubernetes will slowly replace the older pods with the latest pods, one at a time. This strategy uses a zero-downtime deployment technique, ensuring application uptime during updates. Below you can see the demonstration: 

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app

spec:

  replicas: 10

  strategy:

    type: RollingUpdate

    rollingUpdate:

      maxSurge: 2

      maxUnavailable: 1

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:latest

2. Recreate

An important scenario where the “recreate strategy” plays its role is when attaching volume with the “ReadWriteOnce” access mode. Throughout a rolling upgrade, Kubernetes works hard to attach the existing volume to the new pods but fails, hence it could only be attached to a single pod at a time. This can result in downtime, making the 'recreate' strategy a viable alternative in certain scenarios.

Other Features Of The Recreate Strategy

• This is a swift strategy that rapidly replaces old pods with the latest ones. 
• While causing a minimalistic configuration, it also causes some downtime.
• It's perfect for basic deployments or when the downtime caused is acceptable. 

Let's Find Out With An Example

Imagine you have encountered an urgent bug fix that needs to be deployed as soon as possible. Using a recreate strategy, Kubernetes will promptly kill all the old pods and replace them with new ones. This will cause a brief downtime as the new pods are being developed. Below you can see the example: 

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app

spec:

  replicas: 10

  strategy:

    type: Recreate

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:latest

3. Blue/Green (Red/Black)

Leaving the old deployment strategy behind, this new strategy introduces you to the so-called “Blue Version” and then the “Green Version” simultaneously. As a result, you will have the blue and green versions running end to end. 

One of the major drawbacks of this strategy is that you have double the amount of pods that you require. For example, if you have 300 pods available in the blue deployment, you’ll also have 300 pods available in the green deployment. The irony is that only half of these pods are typically used in the process out of all 300. 

Speaking of the perks, there are so many advantages of this strategy that they overshadow the disadvantages. First of all, you can run as many tests as you want on your deployment to see if things are going well. See if things are running as expected and when you are finally happy with the results, you can shift the users to the green deployment. There you reap the advantage of “Instant Immigration”. All you have to do is give the command to reroute the users' traffic from blue to green and there you have it! 

Other Features Of The Blue/Green Deployment Strategy

• Blue/Green strategy deploys the latest version parallel to the old version simultaneously.
• After thorough testing and examination, the traffic is switched to the new version.
• Allows for rollbacks in case of issues but requires more infrastructure.

Let's Understand With An Example

If you’re managing a vast social media platform that has a million followers, in that case, you might like to introduce the latest features yet you’re worried about the potential risks attached to it. Therefore, using this strategy, you can easily deploy new versions with the old ones. After that, you can gradually shift the traffic to the latest version and optimize the performance. In case of any significant issue, you can seamlessly shift back to the more convenient version.  Find below an illustration: 

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app-blue

spec:

  replicas: 10

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:v1

  selector:

    matchLabels:

      app: my-app

      version: blue

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app-green

spec:

  replicas: 10

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:v2

  selector:

    matchLabels:

      app: my-app

      version: green

4. Canary

The Canary deployments route a small group of users to the new versions of applications while operating on a smaller set of pods. 

Instead of the fact that Canary has a high resemblance to blue/green deployments, still is much better, and controlled, and uses the progressive delivery phased-in approach. Plenty of other strategies fall under the category of Canary such as dark launches, or A/B testing. Rather than affecting the entire user base, this strategy tests the functionality on a small group of active users. If something goes wrong, doing this keeps the blast radius contained. 

Therefore, Canary Strategy helps to analyze the impact of the latest features while operating them over a small number of users. Moreover, the possible spillover stays certain to a small group of users only! 

In order to test the functionality of the application in the production environment, this strategy is tested on a small group of users. Once the results of these tests turn out to be satisfactory, the replicas of the modern versions start scaling up replacing the old version gradually. 

Other Worth To Be Noted Cranary Features

• Canary strategy deploys the latest version to a small subside of the active users.
• There’s room for testing and gathering potential feedback before the gigantic release. 
• This strategy proves to be quite effective when there is a need for a cautious rollout. 

Let’s Understand With An Example

In case you’re developing a new search algorithm for your e-commerce website, you might like to test it with a small number of users before making it available to everyone. With a canary deployment, you can deploy the new algorithm to a compact group of users known as “the canary”. You can then track how the canary performs and gather user feedback before deciding whether to roll it out to everyone. Below you can see the illustration: 

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app-canary

spec:

  replicas: 1

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:canary

  selector:

    matchLabels:

      app: my-app

      version: canary

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app

spec:

  replicas: 10

  template:

    spec:

      containers:

      - name: my-app-container

        image: my-app:latest

  selector:

    matchLabels:

      app: my-app

      version: stable

• Flagger for Canary Deployments

With Flagger, you can easily deploy the new recommendation engine to 1% of your users. It also helps majorly in monitoring primary metrics like conversion rates and user satisfaction. Also, If the data looks positive, you can gradually increase the rollout percentage until it reaches all users in time. In case of any potential issue, you can easily roll back to the previous version without impacting the entire user base.

Flagger deliberately decreases the risk of introducing new software versions to the existing deployments. 

Other Features Might Include:

• This is an effective tool that automates the canary deployments. 
• It takes care of traffic routing, canary analysis, and promotions.
• It simplifies the process to the maximum and decreases the manual effort. 

5. Controlled Rollout

These deployment strategies are used to slowly introduce an application to a small number of active users in a production environment. This strategy allows organizations to directly analyze the performance and scope of a fresher version before actually deploying it immediately. In the long run, this strategy helps lessen the risks by fixing the bugs and other possible issues at the very beginning. 

A Controlled Rollout Strategy Also Possesses The Following Features

• The Controlled Rollout strategy provides a way to gradually release a new version of an application to a certain number of users or nodes.
• It permits fine-grained control over the deployment process and enables monitoring and analysis before scaling to the full deployment.

Let's Find Out With An Example

Consider you have a microservice called "my-service" deployed in a Kubernetes cluster, and you want to perform a controlled rollout of a new version. Here you can see the demonstration: 

apiVersion: networking.istio.io/v1alpha3

kind: VirtualService

metadata:

  name: my-service

spec:

  hosts:

  - my-service

  http:

  - route:

    - destination:

        host: my-service

        subset: old-version

      weight: 90

    - destination:

        host: my-service

        subset: new-version

      weight: 10

6. Dark Deployments A/B Testing

A/B testing is a competent way to evaluate two or more versions of an application and finally compare them to determine which one performs better than the other. It is used to test especially in terms of user experience, conversion rates, or other determined metrics. In the context of deployments, A/B testing can be done by routing a portion of the traffic to the new version (version A) and the remaining traffic to the existing version (version B). The performance and user feedback of both versions are compared to make data-driven decisions about the deployment.

Other Features Of Dark Deployments A/B Testing

• Without being evident to the active users, this strategy deploys the latest version of an application. 
• It's quite handy for testing new features and analyzing the user response. 

Let's Consider This Example

You are redesigning the home page of your news website. You wish to compare the performance of two distinct designs through testing. You can secretly deploy both designs to separate user groups through A/B testing or a dark deployment. After that, you can monitor click-through rates and other data to see which design performs better, such as user engagement. See the demonstration below: 

kind: Deployment

metadata:

  name: my-app-design-a

spec:

  replicas: 50

  template:

    spec:

Choosing The Right Strategy

Opting for the right Kubernetes deployment strategy requires lots of critical thinking and minding all these pinpoints given below:

• Analyze the scope and intricacies of your application.
• Evaluate your tolerance for downtime and need for testing.
• Choose the strategy that best balances your requirements.
• Minimize disruptions for end-users.
• Identify and mitigate potential pitfalls.
• Analyze short- and long-term expenses.
• Align with relevant regulations.
• Plan for post-implementation feedback and improvements.

Challenges Of The Kubernetes Deployment

Keeping aside the highlighted perks of Kubernetes Deployments, here are some challenges that should be kept on the agenda as well: 

1. Complications

As a particular product grows, the K8s deployment strategies become more and more complex. As a result of these complications, deploying to Kubernetes demands coordination among various servers and server roles. 

2. Unexpected Downtime

Ensuring minimal downtime for the deployment process can unexpectedly slow down the entire deployment. This often happens because implementing strategies to minimize downtime mostly involves some serious steps or checks to ensure a smooth transition.

3. Infrastructure alterations

In cloud environments, using tools like Google Cloud Deployment Manager or Terraform helps to streamline infrastructure creation and updates. However, despite aiming to simplify the deployment process, this automation can sometimes introduce additional layers of complexity.

4. Observability

Enhancing observability among the Kubernetes environments, including monitoring, logging, and cluster stability, can be a challenge as well.

5. Security

Security is often a leading challenge due to Kubernetes' high complexity and absolute vulnerability. Properly securing CI/CD pipelines, clusters, and networks at scale abruptly can become pretty overwhelming without the appropriate tooling, resources, and expertise. Given this context, understanding the KSPM role in your cybersecurity strategy can streamline your approach to securing Kubernetes by consistently managing access rights, detecting vulnerabilities early on, and ensuring that security practices are tightly integrated throughout the deployment process. 

However, by evaluating and addressing these challenges on time, the management and effectiveness of Kubernetes deployments can be boosted.

Conclusion

By partnering with InvoZone, organizations gain the advantage of efficient, reliable, and highly scalable application deployments in Kubernetes environments. 

It’s pretty prominent how Kubernetes deployment, an open-source container orchestration platform has changed the way organizations deploy, maintain, and scale their applications. 

This flexibility, scalability, and high availability of K8s deployment allow huge DevOps teams to create powerful apps that are adaptable to the constantly shifting conditions needed for DevOps services and solutions. 

 

Frequently Asked Questions

1. What is deployment in Kubernetes?

A Kubernetes Deployment majorly highlights the Kubernetes ways for creating or even upgrading the instances of pods that consist of a containerized application. K8s deployments can help successfully grow the number of replica pods, enable the controlled rollout of new code, or roll back to an earlier deployment version if seen as appropriate.

2. How to delete a deployment in Kubernetes?

In order to delete a deployment in Kubernetes, you can get help from the Kubectl delete deployment command. Here are the steps:

• Open the command prompt and connect to your Kubernetes cluster.
• View a list of deployments with the Kubectl get deployment command. Use the -n  flag to specify the namespace of the deployment.
• Now delete the deployment with the kubectl delete deployment  -n  command. 

3. How to restart a deployment in Kubernetes?

The Kubectl rollout restart command is a quite useful Kubernetes tool that allows programmers to restart a K8s deployment or a group of resources. It initiates a rolling update, effortlessly terminating and re-creating the pods associated with the specified resources.

4. What is the difference between pod and deployment in Kubernetes?

The fundamental unit for running applications in Kubernetes, a pod is the smallest deployment unit. A network container may be part of a larger system that uses shared storage and networking. A pod's lifespan is infinitely variable and can be ended or replaced at any moment. The creation and scaling of pods are managed by a deployment, which is a higher-level resource.

5. What is the default deployment strategy in Kubernetes?

Kubernetes uses the rolling deployment as its default deployment strategy. Refreshing a group of pods without triggering any potential risks or complications can be achieved by bringing this approach into practice. To maintain continuous availability during the update process, it replaces the pods running the old version of the application with the new version one by one. When updating applications in a Kubernetes environment, it is advised to use this default technique because it reduces cluster downtime.

6. How to roll out deployment in Kubernetes?

Deploying applications in Kubernetes is a quite streamlined process. Begin by defining your application's setup in a configuration file using YAML, detailing specifics like the image, ports, and the number of instances (replicas) you require. Now, applying this configuration is as simple as using the kubectl apply -f filename.yaml command. Monitoring the deployment's progress can be done with kubectl get deployments or Kubectl Describe Deployment. 

Updating your app involves modifying the YAML file with any necessary changes, such as a new image version, and reapplying it with the kubectl application. Kubernetes handles these updates seamlessly, gradually transitioning to the new version without disrupting the application's availability.