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oss-2022

README.md

Automating Cloud-native Spark Jobs with Argo Workflows

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The talk can be found here.

Goal of this repository

  • Showcase how Spark Jobs can be orchestrated with Argo Workflows
  • Provide a how-to steps to run all examples on local.

Requirements

  • k3d
  • kubectl
  • helm
  • AWS account (used for second example)
  • Kaggle account (used for second example)

Infra setup

  • Start a new kubernetes cluster (using k3d)
k3d cluster create
  • Install Argo Workflows
kubectl create ns argo
kubectl -n argo apply -f infra/argo-install.yaml
  • Install Spark k8s operator and add permission for Argo Workflows
helm repo add spark-operator https://googlecloudplatform.github.io/spark-on-k8s-operator
helm install spark spark-operator/spark-operator --namespace spark-operator --create-namespace --set webhook.enable=true --set sparkJobNamespace=default --set serviceAccounts.spark.name=spark-driver
kubectl -n argo apply -f infra/argo-rbac.yaml

Demo video

View a recording of this demo on Google Drive here.

How to build and run examples

Scala example

Scala examples are located in scala folder. Examples are using dataset from Kaggle.com. Both examples are pretty basic, they are loading file from file system and are doing simple reduceByKey (summing number of rides per bike type and ride length by bike type). These two examples are combined in simple Argo Workflow DAG and are running in parallel.

How to run Scala example

  • Go to Scala folder
cd scala
  • Build Docker image and import it to Kubernetes cluster
docker build -t argo-spark-integration-scala .
k3d image import argo-spark-integration-scala:latest
  • Connect to Argo Workflows
kubectl -n argo port-forward deploy/argo-server 2746:2746
  • Access Argo on localhost:2746 (or using Argo CLI) and submit workflow-scala.yaml

Python example

Python example is very similar to Scala example. The only difference is that we are not using dataset from local file system and are instead downloading from Kaggle.com and saving it to AWS S3. Everything else is similar to the previous example. This example is creating CronWorkflow and it's requiring Kaggle.com and AWS account.

How to run Python example

  • Go to Python folder
cd python
  • Build and import Docker image.
docker build -t argo-spark-integration-python-download-job -f Dockerfile-python-download-job .
k3d image import argo-spark-integration-python-download-job:latest

docker build -t argo-spark-integration-python-bike-job -f Dockerfile-python-bike-job .
k3d image import argo-spark-integration-python-bike-job:latest
  • Edit workflow-python-config.yaml and enter awsRegion, awsBucketName and fileLocation. After that add ConfigMap to cluster.
kubectl apply -f workflow-python-config.yaml -n argo
kubectl apply -f workflow-python-config.yaml -n default
  • Edit workflow-python-secrets.yaml and enter apiUser, apiPassword (from Keggle.com), awsAccessKeyId and awsSecretKey. After that add Secret to cluster.
kubectl apply -f workflow-python-secrets.yaml -n argo
kubectl apply -f workflow-python-secrets.yaml -n default
  • Connect to Argo Workflows
kubectl -n argo port-forward deploy/argo-server 2746:2746
  • Access Argo on localhost:2746 (or using Argo CLI) and submit cron-workflow-python.yaml

About Pipekit

Pipekit is the control plane for Argo Workflows. Platform teams use Pipekit to manage data & CI pipelines at scale, while giving developers self-serve access to Argo. Pipekit's unified logging view, enterprise-grade RBAC, and multi-cluster management capabilities lower maintenance costs for platform teams while delivering a superior devex for Argo users. Sign up for a 30-day free trial at pipekit.io/signup.

Learn more about Pipekit's professional support for companies already using Argo at pipekit.io/services.