Spark Operations

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 Spark configuration property for a job:

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

For instance, if a node is labeled with fusion_node_type=spark_only, then you would scheduled Spark executor pods to run on that node using:

spark.kubernetes.node.selector.fusion_node_type=spark_only

Tip	Spark version 2.4.x does not support tolerations for Spark pods; consequently, Spark pods cannot be scheduled on any nodes with taints.

Cluster mode

Fusion 5.0 ships with Spark 2.4.3 and operates in "cluster" mode on top of Kubernetes. In cluster mode, each Spark driver runs in a separate pod and hence 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.

Table 1. 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

Table 2. 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`