For a more detailed guide on how to use, compose, and work with SparkApplications, please refer to the
User Guide. If you are running the Kubernetes Operator for Apache Spark on Google Kubernetes Engine and want to use Google Cloud Storage (GCS) and/or BigQuery for reading/writing data, also refer to the GCP guide. The Kubernetes Operator for Apache Spark will simply be referred to as the operator for the rest of this guide.
- Installation
- Running the Examples
- Configuration
- Upgrade
- About the Service Account for Driver Pods
- Enable Metric Exporting to Prometheus
- Driver UI Access and Ingress
- About the Mutating Admission Webhook
To install the operator, use the Helm chart.
$ helm repo add incubator http://storage.googleapis.com/kubernetes-charts-incubator
$ helm install incubator/sparkoperator --namespace spark-operatorInstalling the chart will create a namespace spark-operator if it doesn't exist, set up RBAC for the operator to run in the namespace. It will also set up RBAC for driver pods of your Spark applications to be able to manipulate executor pods. In addition, the chart will create a Deployment in the namespace spark-operator. The chart by default does not enable Mutating Admission Webhook for Spark pod customization. When enabled, a webhook service and a secret storing the x509 certificate called spark-webhook-certs are created for that purpose. To install the operator with the mutating admission webhook on a Kubernetes cluster, install the chart with the flag enableWebhook=true:
$ helm install incubator/sparkoperator --namespace spark-operator --set enableWebhook=trueDue to a known issue in GKE, you will need to first grant yourself cluster-admin privileges before you can create custom roles and role bindings on a GKE cluster versioned 1.6 and up. Run the following command before installing the chart on GKE:
$ kubectl create clusterrolebinding <user>-cluster-admin-binding --clusterrole=cluster-admin --user=<user>@<domain>Now you should see the operator running in the cluster by checking the status of the Helm release.
$ helm status <spark-operator-release-name>To run the Spark Pi example, run the following command:
$ kubectl apply -f examples/spark-pi.yamlNote that spark-pi.yaml configures the driver pod to use the spark service account to communicate with the Kubernetes API server. You might need to replace it with the approprate service account before submitting the job. If you installed the operator using the Helm chart, the Spark job namespace (i.e. default by default) already has a service account you can use. Its name ends with -spark and starts with the Helm release name. The Helm chart has a configuration option called sparkJobNamespace which defaults to default. For example, If you would like to run your Spark job in another namespace called test-ns, then first make sure it already exists and then install the chart with the command:
$ helm install incubator/sparkoperator --namespace spark-operator --set sparkJobNamespace=test-nsThen the chart will set up a service account for your Spark jobs to use in that namespace.
Running the above command will create a SparkApplication object named spark-pi. Check the object by running the following command:
$ kubectl get sparkapplications spark-pi -o=yamlThis will show something similar to the following:
apiVersion: sparkoperator.k8s.io/v1beta1
kind: SparkApplication
metadata:
...
spec:
deps: {}
driver:
coreLimit: 200m
cores: 0.1
labels:
version: 2.3.0
memory: 512m
serviceAccount: spark
executor:
cores: 1
instances: 1
labels:
version: 2.3.0
memory: 512m
image: gcr.io/ynli-k8s/spark:v2.4.0
mainApplicationFile: local:///opt/spark/examples/jars/spark-examples_2.11-2.3.0.jar
mainClass: org.apache.spark.examples.SparkPi
mode: cluster
restartPolicy:
type: OnFailure
onFailureRetries: 3
onFailureRetryInterval: 10
onSubmissionFailureRetries: 5
onSubmissionFailureRetryInterval: 20
type: Scala
status:
sparkApplicationId: spark-5f4ba921c85ff3f1cb04bef324f9154c9
applicationState:
state: COMPLETED
completionTime: 2018-02-20T23:33:55Z
driverInfo:
podName: spark-pi-83ba921c85ff3f1cb04bef324f9154c9-driver
webUIAddress: 35.192.234.248:31064
webUIPort: 31064
webUIServiceName: spark-pi-2402118027-ui-svc
webUIIngressName: spark-pi-ui-ingress
webUIIngressAddress: spark-pi.ingress.cluster.com
executorState:
spark-pi-83ba921c85ff3f1cb04bef324f9154c9-exec-1: COMPLETED
LastSubmissionAttemptTime: 2018-02-20T23:32:27ZTo check events for the SparkApplication object, run the following command:
$ kubectl describe sparkapplication spark-piThis will show the events similarly to the following:
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal SparkApplicationAdded 5m spark-operator SparkApplication spark-pi was added, enqueued it for submission
Normal SparkApplicationTerminated 4m spark-operator SparkApplication spark-pi terminated with state: COMPLETED
The operator submits the Spark Pi example to run once it receives an event indicating the SparkApplication object was added.
The operator is typically deployed and run using the Helm chart. However, users can still run it outside a Kubernetes cluster and make it talk to the Kubernetes API server of a cluster by specifying path to kubeconfig, which can be done using the -kubeconfig flag.
The operator uses multiple workers in the SparkApplication controller. The number of worker threads are controlled using command-line flag -controller-threads which has a default value of 10.
The operator enables cache resynchronization so periodically the informers used by the operator will re-list existing objects it manages and re-trigger resource events. The resynchronization interval in seconds can be configured using the flag -resync-interval, with a default value of 30 seconds.
By default, the operator will install the CustomResourceDefinitions for the custom resources it manages. This can be disabled by setting the flag -install-crds=false, in which case the CustomResourceDefinitions can be installed manually using kubectl apply -f manifest/spark-operator-crds.yaml.
The mutating admission webhook is an optional component and can be enabled or disabled using the -enable-webhook flag, which defaults to false.
By default, the operator will manage custom resource objects of the managed CRD types for the whole cluster. It can be configured to manage only the custom resource objects in a specific namespace with the flag -namespace=<namespace>
To upgrade the the operator, e.g., to use a newer version container image with a new tag, run the following command with updated parameters for the Helm release:
$ helm upgrade <YOUR-HELM-RELEASE-NAME> --set operatorImageName=org/image --set operatorVersion=newTagRefer to the Helm documentation for more details on helm upgrade.
A Spark driver pod need a Kubernetes service account in the pod's namespace that has permissions to create, get, list, and delete executor pods, and create a Kubernetes headless service for the driver. The driver will fail and exit without the service account, unless the default service account in the pod's namespace has the needed permissions. To submit and run a SparkApplication in a namespace, please make sure there is a service account with the permissions in the namespace and set .spec.driver.serviceAccount to the name of the service account. Please refer to spark-rbac.yaml for an example RBAC setup that creates a driver service account named spark in the default namespace, with a RBAC role binding giving the service account the needed permissions.
The operator exposes a set of metrics via the metric endpoint to be scraped by Prometheus. The Helm chart by default installs the operator with the additional flag to enable metrics (-enable-metrics=true) as well as other annotations used by Prometheus to scrape the metric endpoint. To install the operator without metrics enabled, pass the appropriate flag during helm install:
$ helm install incubator/sparkoperator --namespace spark-operator --set enableMetrics=falseIf enabled, the operator generates the following metrics:
| Metric | Description |
|---|---|
spark_app_submit_count |
Total number of SparkApplication submitted by the Operator. |
spark_app_success_count |
Total number of SparkApplication which completed successfully. |
spark_app_failure_count |
Total number of SparkApplication which failed to complete. |
spark_app_running_count |
Total number of SparkApplication which are currently running. |
spark_app_success_execution_time_microseconds |
Execution time for applications which succeeded. |
spark_app_failure_execution_time_microseconds |
Execution time for applications which failed. |
spark_app_executor_success_count |
Total number of Spark Executors which completed successfully. |
spark_app_executor_failure_count |
Total number of Spark Executors which failed. |
spark_app_executor_running_count |
Total number of Spark Executors which are currently running. |
The following is a list of all the configurations the operators supports for metrics:
-enable-metrics=true
-metrics-port=10254
-metrics-endpoint=/metrics
-metrics-prefix=myServiceName
-metrics-label=label1Key
-metrics-label=label2KeyAll configs except -enable-metrics are optional. If port and/or endpoint are specified, please ensure that the annotations prometheus.io/port, prometheus.io/path and containerPort in spark-operator-with-metrics.yaml are updated as well.
A note about metrics-labels: In Prometheus, every unique combination of key-value label pair represents a new time series, which can dramatically increase the amount of data stored. Hence labels should not be used to store dimensions with high cardinality with potentially a large or unbounded value range.
Additionally, these metrics are best-effort for the current operator run and will be reset on an operator restart. Also some of these metrics are generated by listening to pod state updates for the driver/executors and deleting the pods outside the operator might lead to incorrect metric values for some of these metrics.
The operator, by default, makes the Spark UI accessible by creating a service of type ClusterIP which exposes the UI. This is only accessible from within the cluster.
The operator also supports creating an Ingress for the UI. This can be turned on by setting the ingress-url-format command-line flag. The ingress-url-format should be a template like {{$appName}}.ingress.cluster.com and the operator will replace the {{$appName}} with the appropriate appName.
The operator also sets both WebUIAddress which is accessible from within the cluster as well as WebUIIngressAddress as part of the DriverInfo field of the SparkApplication.
The Kubernetes Operator for Apache Spark comes with an optional mutating admission webhook for customizing Spark driver and executor pods based on the specification in SparkApplication objects, e.g., mounting user-specified ConfigMaps and volumes, and setting pod affinity/anti-affinity, and adding tolerations.
The webhook requires a X509 certificate for TLS for pod admission requests and responses between the Kubernetes API server and the webhook server running inside the operator. For that, the certificate and key files must be accessible by the webhook server.
The Kubernetes Operator for Spark ships with a tool at hack/gencerts.sh for generating the CA and server certificate and putting the certificate and key files into a secret named spark-webhook-certs in the namespace spark-operator. This secret will be mounted into the operator pod.
Run the following command to create the secret with a certificate and key files using a batch Job, and install the operator Deployment with the mutating admission webhook:
$ kubectl apply -f manifest/spark-operator-with-webhook.yamlThis will create a Deployment named sparkoperator and a Service named spark-webhook for the webhook in namespace spark-operator.
If the operator is installed via the Helm chart using the default settings (i.e. with webhook enabled), the above steps are all automated for you.