Spark Operations

Table of Contents

Node Selectors
Cluster mode
Spark config defaults

These topics provide how-tos for Spark operations:

Node Selectors

You can control which nodes Spark executors are scheduled on using a Spark configuration property for a job:

spark.kubernetes.node.selector.<LABEL>=<LABEL_VALUE>

Use the LABEL specified for the node, and the name of the node as the LABEL_VALUE. For example, if a node is labeled with fusion_node_type=spark_only, schedule Spark executor pods to run on that node using:

spark.kubernetes.node.selector.fusion_node_type=spark_only

Spark version 2.4.x does not support tolerations for Spark pods. As a result, Spark pods can’t be scheduled on any nodes with taints.

Cluster mode

Fusion 5 ships with Spark and operates in "cluster mode" on top of Kubernetes. In cluster mode, each Spark driver runs in a separate pod, and resources can be managed per job. Each executor also runs in its own pod.

Spark config defaults

The table below shows the default configurations for Spark. These settings are configured in the job-launcher config map, accessible using kubectl get configmaps <release-name>-job-launcher. Some of these settings are also configurable via Helm.

Spark Resource Configurations
Spark Configuration	Default value	Helm Variable
`spark.driver.memory`	3g
`spark.executor.instances`	2	`executorInstances`
`spark.executor.memory`	3g
`spark.executor.cores`	6
`spark.kubernetes.executor.request.cores`	3
`spark.sql.caseSensitive`	true

Spark Kubernetes Configurations
Spark Configuration	Default value	Helm Variable
`spark.kubernetes.container.image.pullPolicy`	Always	`image.imagePullPolicy`
`spark.kubernetes.container.image.pullSecrets`		`image.imagePullSecrets`
`spark.kubernetes.authenticate.driver.serviceAccountName`	<name>-job-launcher-spark
`spark.kubernetes.driver.container.image`	fusion-dev-docker.ci-artifactory.lucidworks.com	`image.repository`
`spark.kubernetes.executor.container.image`	fusion-dev-docker.ci-artifactory.lucidworks.com	`image.repository`