Amazon EMR on EKS

Introduction

Amazon EMR on Amazon EKS allows you to run big data frameworks such as Apache Spark and Apache Hadoop on Kubernetes. This integration provides flexibility, scalability, and cost-effectiveness.

Key Concepts

• **Amazon EMR**: A cloud-native big data platform.
• **Amazon EKS**: A managed Kubernetes service for running containerized applications.
• **Pod**: The smallest deployable unit in Kubernetes.
• **Task**: A unit of work in EMR.

Setup Steps

1. Create an EKS Cluster

To create an EKS cluster, use the AWS Management Console or AWS CLI.

aws eks create-cluster --name my-cluster --role-arn arn:aws:iam::123456789012:role/EKS-ClusterRole --resources-vpc-config subnetIds=subnet-12345678,subnet-87654321

2. Create an EMR on EKS Configuration

Deploy the EMR on EKS configuration using the following command:

aws emr-containers create-virtual-cluster --name "MyVirtualCluster" --container-provider "{ \"type\": \"EKS\", \"id\": \"arn:aws:eks:us-west-2:123456789012:cluster/my-cluster\" }"

3. Submit a Spark Job

Submit a job to your EMR on EKS virtual cluster:

aws emr-containers start-job-run --virtual-cluster-id  --job-driver "{ \"sparkSubmitJobDriver\": { \"entryPoint\": \"s3://your-bucket/your-script.py\" } }"

Best Practices

• Use spot instances to reduce costs.
• Optimize your Spark jobs for performance.
• Enable logging to monitor job performance.
• Regularly update your EKS and EMR configurations.

FAQ