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Demo: Using Cloud Storage for Checkpoint Location in Spark Structured Streaming on Google Kubernetes Engine

This demo is a follow-up to Demo: Running Spark Structured Streaming on minikube and is going to show the steps to use a persistent disk Google Cloud Storage for a checkpoint location in a Spark Structured Streaming application on Google Kubernetes Engine.

The demo uses the Cloud Storage connector that lets Spark applications access data in Cloud Storage using the gs:// prefix.

.option("checkpointLocation", "gs://spark-checkpoint-location/")

The most challenging task in the demo has been to include necessary dependencies in a Docker image to support the gs:// prefix.

Before you begin

It is assumed that you have finished the following:

  1. Demo: Running Spark Structured Streaming on minikube
  2. Demo: Running Spark Examples on Google Kubernetes Engine

Environment Variables

You will need the following environment variables to run the demo. They are all in one section to find them easier when needed (e.g. switching terminals).

export PROJECT_ID=$(gcloud info --format='value(config.project)')

export CLUSTER_NAME=spark-examples-cluster

# Has to end with /
export BUCKET_NAME=gs://spark-on-kubernetes-2021/

export K8S_SERVER=$(kubectl config view --output=jsonpath='{.clusters[].cluster.server}')
export POD_NAME=spark-streams-google-storage-demo
export SPARK_IMAGE=$GCP_CR/spark-streams-google-storage-demo:0.1.0

export K8S_NAMESPACE=spark-demo

export KEY_JSON=spark-on-kubernetes-2021.json
export MOUNT_PATH=/opt/spark/secrets

Build Spark Application Image

List images and make sure that the Spark image is available. If not, follow the steps in Demo: Running Spark Examples on Google Kubernetes Engine.

gcloud container images list --repository $GCP_CR
gcloud container images list-tags $GCP_CR/spark

Go to your Spark application project and build the image.

sbt spark-streams-demo/docker:publishLocal spark-streams-google-storage-demo/docker:publishLocal
docker images $GCP_CR/spark-streams-google-storage-demo
REPOSITORY                                                             TAG       IMAGE ID       CREATED         SIZE   0.1.0     b9dd310765ba   3 minutes ago   542MB
docker tag

Use docker tag unless you've done it already at build time.

docker tag spark-streams-google-storage-demo:0.1.0 $GCP_CR/spark-streams-google-storage-demo:0.1.0

Push Image to Container Registry

Use docker image push to push the Spark application image to the Container Registry on Google Cloud Platform.

docker push $GCP_CR/spark-streams-google-storage-demo:0.1.0

Display Images

List the available images.

gcloud container images list --repository $GCP_CR

Create Kubernetes Cluster

Create a Kubernetes cluster as described in Demo: Running Spark Examples on Google Kubernetes Engine.

gcloud container clusters create $CLUSTER_NAME \

Wait a few minutes before the cluster is ready.

In the end, you should see the messages as follows:

kubeconfig entry generated for spark-examples-cluster.
spark-examples-cluster  europe-west3-b  1.18.15-gke.1100  e2-medium     1.18.15-gke.1100  3          RUNNING

Create Cloud Storage Bucket

Quoting Connecting to Cloud Storage buckets:

Cloud Storage is a flexible, scalable, and durable storage option for your virtual machine instances. You can read and write files to Cloud Storage buckets from almost anywhere, so you can use buckets as common storage between your instances, App Engine, your on-premises systems, and other cloud services.


You may want to review Storage options for alternative instance storage options.

gsutil mb -b on $BUCKET_NAME

List Contents of Bucket

gsutil ls -l $BUCKET_NAME

There should be no output really since you've just created it.

Run Spark Structured Streaming on GKE

Create Kubernetes Resources

Create Kubernetes resources as described in Demo: Running Spark Examples on Google Kubernetes Engine.

k create -f k8s/rbac.yml
namespace/spark-demo created
serviceaccount/spark created created

Create Service Account Credentials

As the Spark application will need access to Google Cloud services, it requires a service account.


Learn more in Authenticating to Google Cloud with service accounts tutorial. The most important section is Creating service account credentials.

You should have a JSON key file containing the credentials of the service account to authenticate the application with.

Import Credentials as Kubernetes Secret


Learn more in Authenticating to Google Cloud with service accounts tutorial. The most important section is Importing credentials as a Secret.

The recommended way of using the JSON key file with the service account in Kubernetes is using Secret resource type.

kubectl create secret generic spark-sa \
  --from-file=key.json=$KEY_JSON \

Configure Spark Application with Kubernetes Secret

In order to use the service account and access the bucket using gs:// URI scheme you are going to use the following additional configuration properties:

--conf spark.kubernetes.driver.secrets.spark-sa=$MOUNT_PATH
--conf spark.kubernetes.executor.secrets.spark-sa=$MOUNT_PATH
--conf spark.kubernetes.driverEnv.GOOGLE_APPLICATION_CREDENTIALS=$MOUNT_PATH/key.json
--conf spark.kubernetes.executorEnv.GOOGLE_APPLICATION_CREDENTIALS=$MOUNT_PATH/key.json

Submit Spark Application

Submit the Spark Structured Streaming application to GKE as described in Demo: Running Spark Structured Streaming on minikube.

You may optionally delete all pods (since we use a fixed name for the demo).

k delete po --all -n $K8S_NAMESPACE
./bin/spark-submit \
  --master k8s://$K8S_SERVER \
  --deploy-mode cluster \
  --name $POD_NAME \
  --class meetup.SparkStreamsApp \
  --conf spark.kubernetes.driver.request.cores=400m \
  --conf spark.kubernetes.executor.request.cores=100m \
  --conf spark.kubernetes.container.image=$SPARK_IMAGE \
  --conf$POD_NAME \
  --conf spark.kubernetes.namespace=$K8S_NAMESPACE \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --conf spark.kubernetes.submission.waitAppCompletion=false \
  --conf spark.kubernetes.driver.secrets.spark-sa=$MOUNT_PATH \
  --conf spark.kubernetes.executor.secrets.spark-sa=$MOUNT_PATH \
  --conf spark.kubernetes.driverEnv.GOOGLE_APPLICATION_CREDENTIALS=$MOUNT_PATH/key.json \
  --conf spark.kubernetes.executorEnv.GOOGLE_APPLICATION_CREDENTIALS=$MOUNT_PATH/key.json \
  --conf \
  --conf$MOUNT_PATH/key.json \
  --conf$PROJECT_ID \
  --verbose \
  local:///opt/spark/jars/meetup.spark-streams-demo-0.1.0.jar $BUCKET_NAME

Installing Google Cloud Storage connector for Hadoop

Learn more in Installing the connector.


Watch the logs of the driver and executor pods.

k logs -f $POD_NAME -n $K8S_NAMESPACE

Observe pods in another terminal.

k get po -w -n $K8S_NAMESPACE

Google Cloud Console

Review the Spark application in the Google Cloud Console of the project:

  1. Workloads
  2. Services & Ingress
  3. Configuration (make sure to use spark-demo namespace)

Services & Ingress

While in Services & Ingress, click the service to enable Spark UI.

  1. Go to Service details and scroll down to the Ports section.

  2. Click PORT FORWARDING button next to spark-ui entry.

Service details and PORT FORWARDING

Kill Spark Application

In the end, you can spark-submit --kill the Spark Structured Streaming application.

./bin/spark-submit \
  --master k8s://$K8S_SERVER \

Clean Up

Delete the bucket.

gsutil rm -r $BUCKET_NAME

Delete cluster resources as described in Demo: Running Spark Examples on Google Kubernetes Engine.

That's it. Congratulations!


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