Running Spark Job on Kubernetes with Docker: Step-by-Step Guide

Link to my docker image in Dockerhub:
https://hub.docker.com/r/uttamraj9/pysparkkube01
Link for my github:
https://github.com/uttamraj9/SparkKubernitiesDocker.git

Step 1: Create docker image and create docker image and run locally
cd to dpyspark folder
cd dpyspark
docker build -t kubepyspark .
docker run kubepyspark
–Tag the docker image
docker tag pysparkkube01 uttamraj9/pysparkkube01:pysparkkube
–push docker image to docker hub
docker push uttamraj9/pysparkkube01:pysparkkube
–deploye docker image in kubernities
kubectl apply -f deployment.yaml
Check the status of the Job:
kubectl get jobs
View detailed information about the Job:
kubectl describe job my-job-new
Check the status of the pods created by the Job:
kubectl get pods
View logs of the pods:
kubectl logs pod-name
Monitor the progress of the Job:
kubectl logs -f pod-name

In this YouTube tutorial, we’ll walk through the process of deploying a PySpark job on Kubernetes using Docker. We’ll start by creating a Docker image locally and running it to ensure everything works as expected. Then, we’ll tag the Docker image and push it to Docker Hub for accessibility.

Next, we’ll deploy the Docker image on Kubernetes using a deployment.yaml file. We’ll demonstrate how to check the status of the Job, view detailed information about it, and monitor the progress of the pods created by the Job. Additionally, we’ll show you how to view logs of the pods to troubleshoot any issues that may arise during deployment.

By following along with this step-by-step guide, you’ll learn how to efficiently deploy PySpark jobs on Kubernetes, leveraging Docker for containerization and Kubernetes for orchestration.

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