Spark

To run your Spark workloads with Spark Operator, apply the Spark Operator specific custom resources. The Spark Operator works with the following kinds of custom resources:

• SparkApplication
• ScheduledSparkApplication

See Spark Operator API documentation for more details.

NOTE: If you need to manage these custom resources and RBAC resources across all clusters in a project, it is recommended you use Project Deployments which enables you to leverage GitOps to deploy the resources. Otherwise, you will need to create the resources manually in each cluster.

Prerequisites

Follow these steps:

1. Deploy your Spark Operator. See the Spark Operator documentation for more information.

2. Ensure the necessary RBAC resources referenced in your custom resources exist, otherwise the custom resources can fail. See the Spark Operator documentation for details.

   • This is an example of commands for you to create the RBAC resources needed in your project namespace:

     export PROJECT_NAMESPACE=<project namespace>
     
     kubectl apply -f - <<EOF
     apiVersion: v1
     kind: ServiceAccount
     metadata:
       name: spark-service-account
       namespace: ${PROJECT_NAMESPACE}
     ---
     apiVersion: rbac.authorization.k8s.io/v1
     kind: Role
     metadata:
       namespace: ${PROJECT_NAMESPACE}
       name: spark-role
     rules:
     - apiGroups: [""]
       resources: ["pods"]
       verbs: ["*"]
     - apiGroups: [""]
       resources: ["services"]
       verbs: ["*"]
     - apiGroups: [""]
       resources: ["configmaps"]
       verbs: ["*"]
     - apiGroups: [""]
       resources: ["persistentvolumeclaims"]
       verbs: ["*"]
     ---
     apiVersion: rbac.authorization.k8s.io/v1
     kind: RoleBinding
     metadata:
       name: spark-role-binding
       namespace: ${PROJECT_NAMESPACE}
     subjects:
     - kind: ServiceAccount
       name: spark-service-account
       namespace: ${PROJECT_NAMESPACE}
     roleRef:
       kind: Role
       name: spark-role
       apiGroup: rbac.authorization.k8s.io
     EOF
     

Deploy a simple SparkApplication

Follow these steps:

1. Create your Project if you don’t already have one.

2. Set the PROJECT_NAMESPACE environment variable to the name of your project’s namespace:

   export PROJECT_NAMESPACE=<project namespace>
   

3. Set the SPARK_SERVICE_ACCOUNT environment variable to one of the following:

   1. ${PROJECT_NAMESPACE}, if you skipped the step in Prerequisites to create RBAC resources.

      # This service account is automatically created when you create a project and has access to everything in the project namespace. 
      export SPARK_SERVICE_ACCOUNT=${PROJECT_NAMESPACE}
      

   2. Or set to spark-service-account

   export SPARK_SERVICE_ACCOUNT=spark-service-account
   

4. Apply the SparkApplication custom resource in your project namespace

   kubectl apply -f - <<EOF
   apiVersion: "sparkoperator.k8s.io/v1beta2"
   kind: SparkApplication
   metadata:
     name: pyspark-pi
     namespace: ${PROJECT_NAMESPACE}
   spec:
     type: Python
     pythonVersion: "3"
     mode: cluster
     image: "gcr.io/spark-operator/spark-py:v3.1.1"
     imagePullPolicy: Always
     mainApplicationFile: local:///opt/spark/examples/src/main/python/pi.py
     sparkVersion: "3.1.1"
     restartPolicy:
       type: OnFailure
       onFailureRetries: 3
       onFailureRetryInterval: 10
       onSubmissionFailureRetries: 5
       onSubmissionFailureRetryInterval: 20
     driver:
       cores: 1
       coreLimit: "1200m"
       memory: "512m"
       labels:
         version: 3.1.1
       serviceAccount: ${SPARK_SERVICE_ACCOUNT}
     executor:
       cores: 1
       instances: 1
       memory: "512m"
       labels:
         version: 3.1.1
   EOF
   

Clean up

Follow these steps:

1. View SparkApplications in all namespaces:

   kubectl get sparkapp -A
   

2. Deleting a specific SparkApplication:

   kubectl -n ${PROJECT_NAMESPACE} delete sparkapp <name of sparkapplication>