Kubernetes For Beginners : 5
Tags: aws, kubernetes, devops, alm
Published: 2023-07-17 08:26:48


## Kubernetes For Beginners : 5

### Application Lifecycle Management (ALM) — Part 2

![](https://cdn-images-1.medium.com/max/5120/1*a7Tp47Jmnx2BGuArHKXkbQ.png)

Hello Everyone,

Let’s continue the series of **“*Kubernetes For Beginners*”**

This is the **Fifth** article of the kubernetes series and we will be covering various concepts related to **Application Lifecycle Management** (ALM) in Kubernetes. This is **part 2** of Application Lifecycle Management (ALM).

In this blog, we will explore Application Lifecycle Management (ALM) in Kubernetes and understand its various components. We will also dive into relevant examples using AWS Elastic Kubernetes Service (EKS), Real world examples to make it easier for everyone.

>  **Scaling Applications -**

* Kubernetes excels at scaling applications based on resource utilization or incoming traffic.

* Horizontal Pod Autoscaling (HPA) allows you to automatically adjust the number of application instances (pods) based on defined metrics.

* This ensures that your application can handle varying workloads efficiently.

* By applying these manifests, you create both the Deployment and HPA in your AWS EKS cluster.

* kubectl apply -f myapp-deployment.yaml

![Deployment](https://cdn-images-1.medium.com/max/2000/1*Z6B1HaumoL0roAfoxBEXqA.png)

* kubectl apply -f myapp-hpa.yaml

![HPA](https://cdn-images-1.medium.com/max/2000/1*K5zRFWQ33s5nDfpyVXyhSQ.png)

**Pros:**

* Efficient Resource Utilization: Scaling applications based on demand ensures optimal utilization of resources, allowing us to handle increased traffic or workload without overprovisioning.

* Improved Performance: Scaling horizontally by adding more instances (pods) enables our application to distribute the load effectively, enhancing performance and responsiveness.

**Cons:**

* Monitoring Complexity: Scaling applications requires monitoring resource utilization and defining appropriate metrics for scaling, which adds complexity to the management and monitoring processes.

* Increased Operational Overhead: Scaling applications involves additional administrative overhead, such as configuring and managing auto-scaling policies, which may require additional effort and expertise.

**AWS EKS Example :**

* If we have a web application experiencing increased traffic, we can define an HPA in Kubernetes to scale the number of pods based on CPU utilization or request latency.

* AWS EKS integrates seamlessly with the underlying infrastructure to provision and manage the required resources.

**Real-world example: Airbnb**

* Airbnb, the online marketplace for lodging and tourism experiences, utilizes Kubernetes for scaling their application infrastructure.

* During peak booking periods, they leverage horizontal pod autoscaling (HPA) to automatically increase the number of application instances based on metrics like CPU utilization or request throughput.

* This allows Airbnb to dynamically allocate resources and ensure optimal performance and responsiveness to handle the increased user demand.

>  **Design Patterns -**

* Design patterns in Kubernetes are reusable solutions to common problems encountered while building and managing applications.

* These patterns provide best practices for achieving scalability, fault tolerance, and maintainability.

![](https://cdn-images-1.medium.com/max/2000/1*i8awDn_Wlz8HrvCPeyT67w.png)

**Pros:**

* Best Practices: Design patterns provide proven solutions to common problems, offering guidance and best practices for building scalable, robust, and maintainable applications.

* Reusability: Design patterns can be reused across different applications, promoting consistency, reducing development time, and facilitating collaboration among development teams.

**Cons:**

* Learning Curve: Understanding and implementing design patterns may require a learning curve, especially for developers who are new to Kubernetes or containerized environments.

* Contextual Appropriateness: Not all design patterns may be suitable for every application. Choosing the right pattern requires careful consideration of the application’s specific requirements and constraints.

**AWS EKS Example :**

* One popular design pattern is the “sidecar pattern.”

* In this pattern, an additional container (sidecar) is added to the pod to extend or enhance the functionality of the main container.

* For instance, we can have a sidecar container responsible for logging or handling data synchronization.

**Real-world example: GitHub**

* GitHub, the popular code hosting and collaboration platform, utilizes various Kubernetes design patterns to manage their containerized services.

* They employ the “sidecar pattern” to deploy additional containers alongside the main application containers.

* For example, GitHub uses a sidecar container for log streaming, collecting and forwarding logs to centralized log management systems. which enhances observability and allows for modular and scalable deployments.

>  **Multi-Container Pods -**

* At times, an application requires multiple containers to work together and share resources within the same pod.

* Kubernetes allows us to define and manage these multi-container pods, enabling complex architectures and facilitating communication between containers.

![](https://cdn-images-1.medium.com/max/2000/1*HAFIT0xCUKbBQcIfEwAA_A.png)

**Pros:**

* Simplified Deployment: Co-locating containers within the same pod streamlines deployment and simplifies the management of interdependent components.

* Efficient Resource Sharing: Multi-container pods allow containers to share resources like network namespaces and volumes, optimizing resource utilization and improving communication between components.

**Cons:**

* Complexity of Interactions: Managing communication and dependencies between containers within a pod can become complex, requiring careful coordination and understanding of container interactions.

* Debugging Challenges: Troubleshooting issues within a multi-container pod may involve debugging multiple containers simultaneously, which can be more challenging than debugging individual containers.

**Example with AWS EKS:**

* Consider an application that consists of a web server container and a separate container for a message queue.

* By creating a multi-container pod in Kubernetes, we can ensure that both containers run together, share network namespaces, and communicate seamlessly.

* AWS EKS orchestrates the deployment and management of these pods effortlessly.

**Real-world example: PayPal**

* PayPal, the global online payments platform, implements multi-container pods in Kubernetes to manage their complex application architecture.

* They use sidecar containers within pods to enhance functionality and enable additional services, such as log aggregation, monitoring, and security-related tasks.

* By leveraging multi-container pods, PayPal simplifies deployment and ensures efficient communication and coordination between various components of their payment processing systems.

>  **Self-Healing Applications -**

* Kubernetes provides mechanisms for self-healing applications, ensuring that they are always running as intended.

* If a pod fails, Kubernetes automatically restarts it, replaces it on a healthy node, and ensures the desired state is maintained.

![](https://cdn-images-1.medium.com/max/2000/1*NYLFoXUibNiV0XCMfSx4zw.png)

**Pros:**

* Improved Availability: Kubernetes’ self-healing capabilities ensure that application instances are automatically restarted or replaced in case of failures, minimizing downtime and improving availability.

* Reduced Manual Intervention: With self-healing mechanisms, manual intervention to recover failed instances or pods is minimized, allowing administrators to focus on other critical tasks.

**Cons:**

* Dependency on Monitoring: Self-healing relies on effective monitoring and detection of failures. Insufficient or ineffective monitoring may result in delayed recovery or missed failures.

* Performance Impact: The process of restarting or replacing failed instances incurs a performance overhead, which may affect the overall performance of the application during recovery periods.

**Example with AWS EKS:**

* Suppose a pod in our application crashes due to an error. Kubernetes, in conjunction with AWS EKS, detects the failure and automatically starts a new pod to replace it.

* This automatic recovery mechanism keeps our application resilient and minimizes downtime.

**Real-world example: Clash of Clans**

* Supercell, a renowned mobile game developer known for games like Clash of Clans and Clash Royale, utilizes Kubernetes for managing their game infrastructure.

* Within their Kubernetes deployment, they leverage self-healing mechanisms to ensure the continuous availability and reliability of their games.

* In the gaming industry, self-healing applications are crucial to maintaining a seamless gaming experience for millions of players. It ensure high availability, and provide an uninterrupted gaming experience for their player community.

### **Conclusion:**

In this **ALM [Part 1](https://dev.to/acloudguy/kubernetes-for-beginners-4-33di) & [Part 2](https://dev.to/acloudguy/kubernetes-for-beginners-5-55bo)** blogs, we have explored the fundamentals of Application Lifecycle Management in Kubernetes.

focusing on concepts such as -

* Rolling updates & Rollbacks.

* Commands, arguments & environment variables

* ConfigMaps

* Scaling applications

* Design patterns

* Multi-container pods

* Self-healing applications.

By using AWS Elastic Kubernetes Service (EKS) examples, Real world implemented solutions [acloudguy.in](https://acloudguy.in/) aimed to simplify these concepts and make them accessible for all learners. With Kubernetes and tools like AWS EKS, managing applications in a containerized environment becomes efficient, scalable, and resilient, ensuring a seamless user experience.

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*I am Kunal Shah, AWS Certified Solutions Architect, helping clients to achieve optimal solutions on the Cloud. Cloud Enabler by choice, DevOps Practitioner having 7+ Years of overall experience in the IT industry.*

*I love to talk about Cloud Technology, DevOps, Digital Transformation, Analytics, Infrastructure, Dev Tools, Operational efficiency, Serverless, Cost Optimization, Cloud Networking & Security.*

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