Awesome

KUDO Spark Operator

Developing

Prerequisites

Required software:

• Docker
• GNU Make 4.2.1 or higher
• sha1sum
• kubectl
• KUDO CLI Plugin 0.15.0 or higher

For test cluster provisioning and Stub Universe artifacts upload valid AWS access credentials required:

• AWS_PROFILE or AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables should be provided

For pulling private repos, a GitHub token is required:

• generate GitHub token and export environment variable with token contents: export GITHUB_TOKEN=<your token>
  • or save the token either to <repo root>/shared/data-services-kudo/.github_token or to ~/.ds_kudo_github_token

Build steps

GNU Make is used as the main build tool and includes the following main targets:

• make cluster-create creates a Konvoy or MKE cluster
• make cluster-destroy creates a Konvoy or MKE cluster
• make clean-all removes all artifacts produced by targets from local filesystem
• make docker-spark builds Spark base image based on Apache Spark 3.0.0
• make docker-operator builds Operator image and Spark base image if it's not built
• make docker-builder builds image with required tools to run tests
• make docker-push publishes Spark base image and Spark Operator image to DockerHub
• make test runs tests suite
• make clean-docker removes all files, created by make during docker build goals execution

A typical workflow looks as following:

make clean-all
make cluster-create
make docker-push 
make test
make cluster-destroy

To run tests on a pre-existing cluster with specified operator and spark images, set KUBECONFIG, SPARK_IMAGE_FULL_NAME and OPERATOR_IMAGE_FULL_NAME variables

make test KUBECONFIG=$HOME/.kube/config \
SPARK_IMAGE_FULL_NAME=mesosphere/spark:spark-3.0.0-hadoop-2.9-k8s \
OPERATOR_IMAGE_FULL_NAME=mesosphere/kudo-spark-operator:3.0.0-1.1.0

Package and Release

Release process is semi-automated and based on Github Actions. To make a new release:

• Copy manifsets and docs for KUDO Spark Operator to the Operators repo, raise a PR and make sure the CI check is successful
• After the PR is merged, create and push a new tag, e.g:

git tag -a v3.0.0-1.1.0 -m "KUDO Spark Operator 3.0.0-1.1.0 release"

Pushing the new tag will trigger release workflow, will build the operator package with KUDO, create a new GH release draft with the package attached to it.

• Verify the new release (draft) is created and operator package attached as a release asset
• Add the release notes and publish the release

Installing and using Spark Operator

Prerequisites

• Kubernetes cluster up and running
• kubectl configured to work with provisioned cluster
• KUDO CLI Plugin 0.15.0 or higher

Installation

To install KUDO Spark Operator, run:

make install

This make target runs install_operator.sh script which will install Spark Operator and create Spark Driver roles defined in specs/spark-driver-rbac.yaml. By default, Operator and Driver roles will be created and configured to run in namespace spark-operator. To change the namespace, provide NAMESPACE parameter to make:

make install NAMESPACE=test-namespace

Submitting Spark Application

To submit Spark Application and check its status run:

#switch to operator namespace, e.g.
kubens spark-operator

# create Spark application
kubectl create -f specs/spark-application.yaml

# list applications
kubectl get sparkapplication

# check application status
kubectl describe sparkapplication mock-task-runner

To get started with your app monitoring, please, see also monitoring documentation