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[03] – Deploy Apache Spark with Kubernetes (K8s)

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Apache Spark is one of the most used distributed engines to deal with large amounts of data. Multiple tools can be used to run Spark: Spark Standalone, Apache Hadoop Yarn, Apache Mesos, and Kubernetes.

In this hands-on article, we’ll show how to use spark-k8s-operator to run Spark on Kubernetes.

Prerequisites #

Before you start, make sure that the following tools are installed on your local system and that you have a fundamental understanding of Kubernetes and Spark.

Deploy spark-operator on the cluster #

To get started, add spark-operator to Helm repositories by running the following command.

helm repo add spark-operator https://googlecloudplatform.github.io/spark-on-k8s-operator

Now, run the command below to install the spark-operator Helm chart.

helm install spark-on-k8s spark-operator/spark-operator --namespace spark-operator --create-namespace

Create service account #

In this step, we will create a service account named spark which the spark-operator will use to connect to the Kubernetes Server API. Here’s how to set it up

  • Generate an rbac.yaml file and insert the following content into it.
# Create spark service account
apiVersion: v1
kind: ServiceAccount
metadata:
  name: spark
  namespace: default
---
# Create spark-role
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: default
  name: spark-role
rules:
- apiGroups: [""]
  resources: ["pods", "configmap"]
  verbs: ["*"]
- apiGroups: [""]
  resources: ["services"]
  verbs: ["*"]
- apiGroups: [""]
  resources: ["configmaps"]
  verbs: ["create", "get", "watch", "list"]
---
# Bind spark service account to spark-role
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: spark-role-binding
  namespace: default
subjects:
- kind: ServiceAccount
  name: spark
  namespace: default
roleRef:
  kind: Role
  name: spark-role
  apiGroup: rbac.authorization.k8s.io
  • Deploy the service account using the following command:
kubectl apply -f rbac.yaml

Deloy spark-pi application #

In the example below, we will show you how to install Spark Operator to test the execution of the Spark Pi application in Kubernetes. Apply the YAML manifest file below.

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: pyspark-pi
  namespace: default
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
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    labels:
      version: 3.1.1

Submit the application #

To submit the Spark application apply the manifest file created above as follows:

kubectl apply -f spark-pi.yaml

To verify that your Spark application has been successfully submitted, run:

kubectl get sparkapplications

Within a few moments, you should get an application named spark-pi, its status indicated as RUNNING.

NAME       STATUS   ATTEMPTS   START                  FINISH      AGE
spark-pi   RUNNING  1         2023-09-25T15:41:14Z   <no value>  91s

Monitoring #

After deploying the application, a service named spark-pi-ui-svc is created, hosting the Spark UI. Access it by forwarding the pod’s port to your local environment:

kubectl port-forward svc/spark-pi-ui-svc 4040

Now, you can access the Spark UI in your browser using the URL http://localhost:4040 and you should get the screen below.

Deploy Apache Spark with Kubernetes (K8s)
Spark UI

Cleanup #

If you need to uninstall the spark-operator Helm Charts Release, use the following command:

helm uninstall spark-operator

Conclusion #

With these steps, you’ve successfully set up and deployed Apache Spark on a Kubernetes cluster using the spark-k8s-operator. This powerful combination empowers you to run Spark applications efficiently and manage them seamlessly. Explore the capabilities and possibilities of Spark on Kubernetes for your data processing needs.

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