Running Spark on Kubernetes

• Prerequisites
• How it works
• Submitting Applications to Kubernetes
  • Docker Images
  • Cluster Mode
  • Dependency Management
    • Using Remote Dependencies
  • Secret Management
  • Introspection and Debugging
    • Accessing Logs
    • Accessing Driver UI
    • Debugging
  • Kubernetes Features
    • Namespaces
    • RBAC
  • Client Mode
  • Future Work
• Configuration
  • Spark Properties

Spark can run on clusters managed by Kubernetes. This feature makes use of native Kubernetes scheduler that has been added to Spark.

The Kubernetes scheduler is currently experimental. In future versions, there may be behavioral changes around configuration, container images and entrypoints.

Prerequisites

• A runnable distribution of Spark 2.3 or above.
• A running Kubernetes cluster at version >= 1.6 with access configured to it using kubectl. If you do not already have a working Kubernetes cluster, you may setup a test cluster on your local machine using minikube.
  • We recommend using the latest release of minikube with the DNS addon enabled.
  • Be aware that the default minikube configuration is not enough for running Spark applications. We recommend 3 CPUs and 4g of memory to be able to start a simple Spark application with a single executor.
• You must have appropriate permissions to list, create, edit and delete pods in your cluster. You can verify that you can list these resources by running kubectl auth can-i <list|create|edit|delete> pods.
  • The service account credentials used by the driver pods must be allowed to create pods, services and configmaps.
• You must have Kubernetes DNS configured in your cluster.

How it works

spark-submit can be directly used to submit a Spark application to a Kubernetes cluster. The submission mechanism works as follows:

• Spark creates a Spark driver running within a Kubernetes pod.
• The driver creates executors which are also running within Kubernetes pods and connects to them, and executes application code.
• When the application completes, the executor pods terminate and are cleaned up, but the driver pod persists logs and remains in “completed” state in the Kubernetes API until it’s eventually garbage collected or manually cleaned up.

Note that in the completed state, the driver pod does not use any computational or memory resources.

The driver and executor pod scheduling is handled by Kubernetes. It is possible to schedule the driver and executor pods on a subset of available nodes through a node selector using the configuration property for it. It will be possible to use more advanced scheduling hints like node/pod affinities in a future release.

Submitting Applications to Kubernetes

Docker Images

Kubernetes requires users to supply images that can be deployed into containers within pods. The images are built to be run in a container runtime environment that Kubernetes supports. Docker is a container runtime environment that is frequently used with Kubernetes. Spark (starting with version 2.3) ships with a Dockerfile that can be used for this purpose, or customized to match an individual application’s needs. It can be found in the kubernetes/dockerfiles/ directory.

Spark also ships with a bin/docker-image-tool.sh script that can be used to build and publish the Docker images to use with the Kubernetes backend.

Example usage is:

$ ./bin/docker-image-tool.sh -r <repo> -t my-tag build
$ ./bin/docker-image-tool.sh -r <repo> -t my-tag push

Cluster Mode

To launch Spark Pi in cluster mode,

$ bin/spark-submit \
    --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
    --deploy-mode cluster \
    --name spark-pi \
    --class org.apache.spark.examples.SparkPi \
    --conf spark.executor.instances=5 \
    --conf spark.kubernetes.container.image=<spark-image> \
    local:///path/to/examples.jar

The Spark master, specified either via passing the --master command line argument to spark-submit or by setting spark.master in the application’s configuration, must be a URL with the format k8s://<api_server_url>. Prefixing the master string with k8s:// will cause the Spark application to launch on the Kubernetes cluster, with the API server being contacted at api_server_url. If no HTTP protocol is specified in the URL, it defaults to https. For example, setting the master to k8s://example.com:443 is equivalent to setting it to k8s://https://example.com:443, but to connect without TLS on a different port, the master would be set to k8s://http://example.com:8080.

In Kubernetes mode, the Spark application name that is specified by spark.app.name or the --name argument to spark-submit is used by default to name the Kubernetes resources created like drivers and executors. So, application names must consist of lower case alphanumeric characters, -, and . and must start and end with an alphanumeric character.

If you have a Kubernetes cluster setup, one way to discover the apiserver URL is by executing kubectl cluster-info.

$ kubectl cluster-info
Kubernetes master is running at http://127.0.0.1:6443

In the above example, the specific Kubernetes cluster can be used with spark-submit by specifying --master k8s://http://127.0.0.1:6443 as an argument to spark-submit. Additionally, it is also possible to use the authenticating proxy, kubectl proxy to communicate to the Kubernetes API.

The local proxy can be started by:

$ kubectl proxy

If the local proxy is running at localhost:8001, --master k8s://http://127.0.0.1:8001 can be used as the argument to spark-submit. Finally, notice that in the above example we specify a jar with a specific URI with a scheme of local://. This URI is the location of the example jar that is already in the Docker image.

Dependency Management

If your application’s dependencies are all hosted in remote locations like HDFS or HTTP servers, they may be referred to by their appropriate remote URIs. Also, application dependencies can be pre-mounted into custom-built Docker images. Those dependencies can be added to the classpath by referencing them with local:// URIs and/or setting the SPARK_EXTRA_CLASSPATH environment variable in your Dockerfiles. The local:// scheme is also required when referring to dependencies in custom-built Docker images in spark-submit. Note that using application dependencies from the submission client’s local file system is currently not yet supported.

Using Remote Dependencies

When there are application dependencies hosted in remote locations like HDFS or HTTP servers, the driver and executor pods need a Kubernetes init-container for downloading the dependencies so the driver and executor containers can use them locally.

The init-container handles remote dependencies specified in spark.jars (or the --jars option of spark-submit) and spark.files (or the --files option of spark-submit). It also handles remotely hosted main application resources, e.g., the main application jar. The following shows an example of using remote dependencies with the spark-submit command:

$ bin/spark-submit \
    --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
    --deploy-mode cluster \
    --name spark-pi \
    --class org.apache.spark.examples.SparkPi \
    --jars https://path/to/dependency1.jar,https://path/to/dependency2.jar
    --files hdfs://host:port/path/to/file1,hdfs://host:port/path/to/file2
    --conf spark.executor.instances=5 \
    --conf spark.kubernetes.container.image=<spark-image> \
    https://path/to/examples.jar

Secret Management

Kubernetes Secrets can be used to provide credentials for a Spark application to access secured services. To mount a user-specified secret into the driver container, users can use the configuration property of the form spark.kubernetes.driver.secrets.[SecretName]=<mount path>. Similarly, the configuration property of the form spark.kubernetes.executor.secrets.[SecretName]=<mount path> can be used to mount a user-specified secret into the executor containers. Note that it is assumed that the secret to be mounted is in the same namespace as that of the driver and executor pods. For example, to mount a secret named spark-secret onto the path /etc/secrets in both the driver and executor containers, add the following options to the spark-submit command:

--conf spark.kubernetes.driver.secrets.spark-secret=/etc/secrets
--conf spark.kubernetes.executor.secrets.spark-secret=/etc/secrets

Note that if an init-container is used, any secret mounted into the driver container will also be mounted into the init-container of the driver. Similarly, any secret mounted into an executor container will also be mounted into the init-container of the executor.

Introspection and Debugging

These are the different ways in which you can investigate a running/completed Spark application, monitor progress, and take actions.

Accessing Logs

Logs can be accessed using the Kubernetes API and the kubectl CLI. When a Spark application is running, it’s possible to stream logs from the application using:

$ kubectl -n=<namespace> logs -f <driver-pod-name>

The same logs can also be accessed through the Kubernetes dashboard if installed on the cluster.

Accessing Driver UI

The UI associated with any application can be accessed locally using kubectl port-forward.

$ kubectl port-forward <driver-pod-name> 4040:4040

Then, the Spark driver UI can be accessed on http://localhost:4040.

Debugging

There may be several kinds of failures. If the Kubernetes API server rejects the request made from spark-submit, or the connection is refused for a different reason, the submission logic should indicate the error encountered. However, if there are errors during the running of the application, often, the best way to investigate may be through the Kubernetes CLI.

To get some basic information about the scheduling decisions made around the driver pod, you can run:

$ kubectl describe pod <spark-driver-pod>

If the pod has encountered a runtime error, the status can be probed further using:

$ kubectl logs <spark-driver-pod>

Status and logs of failed executor pods can be checked in similar ways. Finally, deleting the driver pod will clean up the entire spark application, including all executors, associated service, etc. The driver pod can be thought of as the Kubernetes representation of the Spark application.

Kubernetes Features

Namespaces

Kubernetes has the concept of namespaces. Namespaces are ways to divide cluster resources between multiple users (via resource quota). Spark on Kubernetes can use namespaces to launch Spark applications. This can be made use of through the spark.kubernetes.namespace configuration.

Kubernetes allows using ResourceQuota to set limits on resources, number of objects, etc on individual namespaces. Namespaces and ResourceQuota can be used in combination by administrator to control sharing and resource allocation in a Kubernetes cluster running Spark applications.

RBAC

In Kubernetes clusters with RBAC enabled, users can configure Kubernetes RBAC roles and service accounts used by the various Spark on Kubernetes components to access the Kubernetes API server.

The Spark driver pod uses a Kubernetes service account to access the Kubernetes API server to create and watch executor pods. The service account used by the driver pod must have the appropriate permission for the driver to be able to do its work. Specifically, at minimum, the service account must be granted a Role or ClusterRole that allows driver pods to create pods and services. By default, the driver pod is automatically assigned the default service account in the namespace specified by spark.kubernetes.namespace, if no service account is specified when the pod gets created.

Depending on the version and setup of Kubernetes deployed, this default service account may or may not have the role that allows driver pods to create pods and services under the default Kubernetes RBAC policies. Sometimes users may need to specify a custom service account that has the right role granted. Spark on Kubernetes supports specifying a custom service account to be used by the driver pod through the configuration property spark.kubernetes.authenticate.driver.serviceAccountName=<service account name>. For example to make the driver pod use the spark service account, a user simply adds the following option to the spark-submit command:

--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark

To create a custom service account, a user can use the kubectl create serviceaccount command. For example, the following command creates a service account named spark:

$ kubectl create serviceaccount spark

To grant a service account a Role or ClusterRole, a RoleBinding or ClusterRoleBinding is needed. To create a RoleBinding or ClusterRoleBinding, a user can use the kubectl create rolebinding (or clusterrolebinding for ClusterRoleBinding) command. For example, the following command creates an edit ClusterRole in the default namespace and grants it to the spark service account created above:

$ kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=default:spark --namespace=default

Note that a Role can only be used to grant access to resources (like pods) within a single namespace, whereas a ClusterRole can be used to grant access to cluster-scoped resources (like nodes) as well as namespaced resources (like pods) across all namespaces. For Spark on Kubernetes, since the driver always creates executor pods in the same namespace, a Role is sufficient, although users may use a ClusterRole instead. For more information on RBAC authorization and how to configure Kubernetes service accounts for pods, please refer to Using RBAC Authorization and Configure Service Accounts for Pods.

Client Mode

Client mode is not currently supported.

Future Work

There are several Spark on Kubernetes features that are currently being incubated in a fork - apache-spark-on-k8s/spark, which are expected to eventually make it into future versions of the spark-kubernetes integration.

Some of these include:

• PySpark
• R
• Dynamic Executor Scaling
• Local File Dependency Management
• Spark Application Management
• Job Queues and Resource Management

You can refer to the documentation if you want to try these features and provide feedback to the development team.

Configuration

See the configuration page for information on Spark configurations. The following configurations are specific to Spark on Kubernetes.

Spark Properties

Property Name	Default	Meaning
spark.kubernetes.namespace	default	The namespace that will be used for running the driver and executor pods.
spark.kubernetes.container.image	(none)	Container image to use for the Spark application. This is usually of the form example.com/repo/spark:v1.0.0. This configuration is required and must be provided by the user, unless explicit images are provided for each different container type.
spark.kubernetes.driver.container.image	(value of spark.kubernetes.container.image)	Custom container image to use for the driver.
spark.kubernetes.executor.container.image	(value of spark.kubernetes.container.image)	Custom container image to use for executors.
spark.kubernetes.container.image.pullPolicy	IfNotPresent	Container image pull policy used when pulling images within Kubernetes.
spark.kubernetes.allocation.batch.size	5	Number of pods to launch at once in each round of executor pod allocation.
spark.kubernetes.allocation.batch.delay	1s	Time to wait between each round of executor pod allocation. Specifying values less than 1 second may lead to excessive CPU usage on the spark driver.
spark.kubernetes.authenticate.submission.caCertFile	(none)	Path to the CA cert file for connecting to the Kubernetes API server over TLS when starting the driver. This file must be located on the submitting machine's disk. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.submission.clientKeyFile	(none)	Path to the client key file for authenticating against the Kubernetes API server when starting the driver. This file must be located on the submitting machine's disk. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.submission.clientCertFile	(none)	Path to the client cert file for authenticating against the Kubernetes API server when starting the driver. This file must be located on the submitting machine's disk. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.submission.oauthToken	(none)	OAuth token to use when authenticating against the Kubernetes API server when starting the driver. Note that unlike the other authentication options, this is expected to be the exact string value of the token to use for the authentication.
spark.kubernetes.authenticate.submission.oauthTokenFile	(none)	Path to the OAuth token file containing the token to use when authenticating against the Kubernetes API server when starting the driver. This file must be located on the submitting machine's disk. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.caCertFile	(none)	Path to the CA cert file for connecting to the Kubernetes API server over TLS from the driver pod when requesting executors. This file must be located on the submitting machine's disk, and will be uploaded to the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.clientKeyFile	(none)	Path to the client key file for authenticating against the Kubernetes API server from the driver pod when requesting executors. This file must be located on the submitting machine's disk, and will be uploaded to the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme). If this is specified, it is highly recommended to set up TLS for the driver submission server, as this value is sensitive information that would be passed to the driver pod in plaintext otherwise.
spark.kubernetes.authenticate.driver.clientCertFile	(none)	Path to the client cert file for authenticating against the Kubernetes API server from the driver pod when requesting executors. This file must be located on the submitting machine's disk, and will be uploaded to the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.oauthToken	(none)	OAuth token to use when authenticating against the Kubernetes API server from the driver pod when requesting executors. Note that unlike the other authentication options, this must be the exact string value of the token to use for the authentication. This token value is uploaded to the driver pod. If this is specified, it is highly recommended to set up TLS for the driver submission server, as this value is sensitive information that would be passed to the driver pod in plaintext otherwise.
spark.kubernetes.authenticate.driver.oauthTokenFile	(none)	Path to the OAuth token file containing the token to use when authenticating against the Kubernetes API server from the driver pod when requesting executors. Note that unlike the other authentication options, this file must contain the exact string value of the token to use for the authentication. This token value is uploaded to the driver pod. If this is specified, it is highly recommended to set up TLS for the driver submission server, as this value is sensitive information that would be passed to the driver pod in plaintext otherwise.
spark.kubernetes.authenticate.driver.mounted.caCertFile	(none)	Path to the CA cert file for connecting to the Kubernetes API server over TLS from the driver pod when requesting executors. This path must be accessible from the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.mounted.clientKeyFile	(none)	Path to the client key file for authenticating against the Kubernetes API server from the driver pod when requesting executors. This path must be accessible from the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.mounted.clientCertFile	(none)	Path to the client cert file for authenticating against the Kubernetes API server from the driver pod when requesting executors. This path must be accessible from the driver pod. Specify this as a path as opposed to a URI (i.e. do not provide a scheme).
spark.kubernetes.authenticate.driver.mounted.oauthTokenFile	(none)	Path to the file containing the OAuth token to use when authenticating against the Kubernetes API server from the driver pod when requesting executors. This path must be accessible from the driver pod. Note that unlike the other authentication options, this file must contain the exact string value of the token to use for the authentication.
spark.kubernetes.authenticate.driver.serviceAccountName	default	Service account that is used when running the driver pod. The driver pod uses this service account when requesting executor pods from the API server. Note that this cannot be specified alongside a CA cert file, client key file, client cert file, and/or OAuth token.
spark.kubernetes.driver.label.[LabelName]	(none)	Add the label specified by LabelName to the driver pod. For example, spark.kubernetes.driver.label.something=true. Note that Spark also adds its own labels to the driver pod for bookkeeping purposes.
spark.kubernetes.driver.annotation.[AnnotationName]	(none)	Add the annotation specified by AnnotationName to the driver pod. For example, spark.kubernetes.driver.annotation.something=true.
spark.kubernetes.executor.label.[LabelName]	(none)	Add the label specified by LabelName to the executor pods. For example, spark.kubernetes.executor.label.something=true. Note that Spark also adds its own labels to the driver pod for bookkeeping purposes.
spark.kubernetes.executor.annotation.[AnnotationName]	(none)	Add the annotation specified by AnnotationName to the executor pods. For example, spark.kubernetes.executor.annotation.something=true.
spark.kubernetes.driver.pod.name	(none)	Name of the driver pod. If not set, the driver pod name is set to "spark.app.name" suffixed by the current timestamp to avoid name conflicts.
spark.kubernetes.executor.lostCheck.maxAttempts	10	Number of times that the driver will try to ascertain the loss reason for a specific executor. The loss reason is used to ascertain whether the executor failure is due to a framework or an application error which in turn decides whether the executor is removed and replaced, or placed into a failed state for debugging.
spark.kubernetes.submission.waitAppCompletion	true	In cluster mode, whether to wait for the application to finish before exiting the launcher process. When changed to false, the launcher has a "fire-and-forget" behavior when launching the Spark job.
spark.kubernetes.report.interval	1s	Interval between reports of the current Spark job status in cluster mode.
spark.kubernetes.driver.limit.cores	(none)	Specify the hard CPU [limit](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) for the driver pod.
spark.kubernetes.executor.limit.cores	(none)	Specify the hard CPU [limit](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) for each executor pod launched for the Spark Application.
spark.kubernetes.node.selector.[labelKey]	(none)	Adds to the node selector of the driver pod and executor pods, with key labelKey and the value as the configuration's value. For example, setting spark.kubernetes.node.selector.identifier to myIdentifier will result in the driver pod and executors having a node selector with key identifier and value myIdentifier. Multiple node selector keys can be added by setting multiple configurations with this prefix.
spark.kubernetes.driverEnv.[EnvironmentVariableName]	(none)	Add the environment variable specified by EnvironmentVariableName to the Driver process. The user can specify multiple of these to set multiple environment variables.
spark.kubernetes.mountDependencies.jarsDownloadDir	/var/spark-data/spark-jars	Location to download jars to in the driver and executors. This directory must be empty and will be mounted as an empty directory volume on the driver and executor pods.
spark.kubernetes.mountDependencies.filesDownloadDir	/var/spark-data/spark-files	Location to download jars to in the driver and executors. This directory must be empty and will be mounted as an empty directory volume on the driver and executor pods.
spark.kubernetes.mountDependencies.timeout	300s	Timeout in seconds before aborting the attempt to download and unpack dependencies from remote locations into the driver and executor pods.
spark.kubernetes.mountDependencies.maxSimultaneousDownloads	5	Maximum number of remote dependencies to download simultaneously in a driver or executor pod.
spark.kubernetes.initContainer.image	(value of spark.kubernetes.container.image)	Custom container image for the init container of both driver and executors.
spark.kubernetes.driver.secrets.[SecretName]	(none)	Add the Kubernetes Secret named SecretName to the driver pod on the path specified in the value. For example, spark.kubernetes.driver.secrets.spark-secret=/etc/secrets. Note that if an init-container is used, the secret will also be added to the init-container in the driver pod.
spark.kubernetes.executor.secrets.[SecretName]	(none)	Add the Kubernetes Secret named SecretName to the executor pod on the path specified in the value. For example, spark.kubernetes.executor.secrets.spark-secret=/etc/secrets. Note that if an init-container is used, the secret will also be added to the init-container in the executor pod.