Advanced Topics in EKS

In this section, we will explore advanced topics and use cases in EKS (Elastic Kubernetes Service) on AWS. These topics are designed for experienced developers who are already familiar with cloud computing and programming design architecture.

1. Custom Resource Definitions (CRDs)

Custom Resource Definitions (CRDs) allow you to extend the Kubernetes API by defining your own resource types. This enables you to create and manage custom resources that are specific to your application or business needs. With CRDs, you can define new object types, specify their behavior, and interact with them using standard Kubernetes tools and mechanisms.

Here's an example of a CRD definition in YAML:

1apiVersion: apiextensions.k8s.io/v1
2kind: CustomResourceDefinition
3metadata:
4  name: myresource.example.com
5spec:
6  group: example.com
7  names:
8    kind: MyResource
9    singular: myresource
10    plural: myresources
11    shortNames:
12      - mr
13  scope: Namespaced
14  version: v1

2. Istio Integration

Istio is an open-source service mesh that provides advanced networking and security capabilities for microservices running on Kubernetes. With Istio, you can easily manage traffic, enforce policies, and secure communication between services in your EKS cluster. By integrating Istio with EKS, you can gain fine-grained control over service-to-service communication, implement advanced routing and load balancing strategies, and enhance observability and resiliency.

3. Scaling Strategies

Scaling is a fundamental aspect of managing applications in Kubernetes. In EKS, you can utilize various scaling strategies to ensure that your applications can handle varying workload demands. These strategies include vertical scaling, horizontal scaling, and cluster autoscaling.

Vertical scaling involves increasing or decreasing the resources (CPU, memory) allocated to individual pods or containers. This can be done dynamically based on resource utilization metrics or manually through configuration.

Horizontal scaling involves adding or removing pods or containers to distribute the workload across multiple instances. This can be done manually or automatically using Kubernetes deployments or replica sets.

Cluster autoscaling automatically adjusts the size of the EKS cluster based on the workload. It adds or removes worker nodes to accommodate the resource requirements of the running pods.

By implementing effective scaling strategies, you can ensure that your EKS cluster is optimized for performance and cost-efficiency.

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  public static void main(String[] args) {

    // Replace with your Java logic here

    for(int i = 1; i <= 100; i++) {

      if(i % 3 == 0 && i % 5 == 0) {

          System.out.println("FizzBuzz");

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          System.out.println("Buzz");

      } else {

          System.out.println(i);

      }

    }    

  }

}