The Kubernetes Operator for Apache Spark aims to make specifying and running Spark applications as easy and idiomatic as running other workloads on Kubernetes. It uses Kubernetes custom resources for specifying, running, and surfacing status of Spark applications. For a complete reference of the custom resource definitions, please refer to the API Definition. For details on its design, please refer to the design doc. It requires Spark 2.3 and above that supports Kubernetes as a native scheduler backend.

The Kubernetes Operator for Apache Spark currently supports the following list of features:

• Supports Spark 2.3 and up.
• Enables declarative application specification and management of applications through custom resources.
• Automatically runs spark-submit on behalf of users for each SparkApplication eligible for submission.
• Provides native cron support for running scheduled applications.
• Supports customization of Spark pods beyond what Spark natively is able to do through the mutating admission webhook, e.g., mounting ConfigMaps and volumes, and setting pod affinity/anti-affinity.
• Supports automatic application re-submission for updated SparkApplication objects with updated specification.
• Supports automatic application restart with a configurable restart policy.
• Supports automatic retries of failed submissions with optional linear back-off.
• Supports mounting local Hadoop configuration as a Kubernetes ConfigMap automatically via sparkctl.
• Supports automatically staging local application dependencies to Google Cloud Storage (GCS) via sparkctl.
• Supports collecting and exporting application-level metrics and driver/executor metrics to Prometheus.

Project status: beta

Current API version: v1beta2

If you are currently using the v1beta1 version of the APIs in your manifests, please update them to use the v1beta2 version by changing apiVersion: "sparkoperator.k8s.io/<version>" to apiVersion: "sparkoperator.k8s.io/v1beta2". You will also need to delete the previous version of the CustomResourceDefinitions named sparkapplications.sparkoperator.k8s.io and scheduledsparkapplications.sparkoperator.k8s.io, and replace them with the v1beta2 version either by installing the latest version of the operator or by running kubectl create -f manifest/crds.

Customization of Spark pods, e.g., mounting arbitrary volumes and setting pod affinity, is implemented using a Kubernetes Mutating Admission Webhook, which became beta in Kubernetes 1.9. The mutating admission webhook is disabled by default if you install the operator using the Helm chart. Check out the Quick Start Guide on how to enable the webhook.

• Version >= 1.13 of Kubernetes to use the subresource support for CustomResourceDefinitions, which became beta in 1.13 and is enabled by default in 1.13 and higher.

• Version >= 1.16 of Kubernetes to use the MutatingWebhook and ValidatingWebhook of apiVersion: admissionregistration.k8s.io/v1.

The easiest way to install the Kubernetes Operator for Apache Spark is to use the Helm chart.

$ helm repo add spark-operator https://kubeflow.github.io/spark-operator

$ helm install my-release spark-operator/spark-operator --namespace spark-operator --create-namespace

This will install the Kubernetes Operator for Apache Spark into the namespace spark-operator. The operator by default watches and handles SparkApplications in every namespaces. If you would like to limit the operator to watch and handle SparkApplications in a single namespace, e.g., default instead, add the following option to the helm install command:

--set sparkJobNamespace=default

For configuration options available in the Helm chart, please refer to the chart's README.

The following table lists the most recent few versions of the operator.

When installing using the Helm chart, you can choose to use a specific image tag instead of the default one, using the following option:

--set image.tag=<operator image tag>

Get started quickly with the Kubernetes Operator for Apache Spark using the Quick Start 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.

For more information, check the Design, API Specification and detailed User Guide.

Please check CONTRIBUTING.md and the Developer Guide out.

• Join our Kubeflow Slack Channel
• Check out who is using the Kubernetes Operator for Apache Spark.