pulumi-gcp-kotlin/com.pulumi.gcp.dataproc.kotlin.inputs/WorkflowTemplateJobSparkRJobArgs

WorkflowTemplateJobSparkRJobArgs

data class WorkflowTemplateJobSparkRJobArgs(val archiveUris: Output<List<String>>? = null, val args: Output<List<String>>? = null, val fileUris: Output<List<String>>? = null, val loggingConfig: Output<WorkflowTemplateJobSparkRJobLoggingConfigArgs>? = null, val mainRFileUri: Output<String>, val properties: Output<Map<String, String>>? = null) : ConvertibleToJava<WorkflowTemplateJobSparkRJobArgs>

Constructors

fun WorkflowTemplateJobSparkRJobArgs(archiveUris: Output<List<String>>? = null, args: Output<List<String>>? = null, fileUris: Output<List<String>>? = null, loggingConfig: Output<WorkflowTemplateJobSparkRJobLoggingConfigArgs>? = null, mainRFileUri: Output<String>, properties: Output<Map<String, String>>? = null)

Functions

toJava

open override fun toJava(): WorkflowTemplateJobSparkRJobArgs

Properties

archiveUris

val archiveUris: Output<List<String>>? = null

HCFS URIs of archives to be extracted into the working directory of each executor. Supported file types: .jar, .tar, .tar.gz, .tgz, and .zip.

args

val args: Output<List<String>>? = null

The arguments to pass to the driver. Do not include arguments, such as --conf, that can be set as job properties, since a collision may occur that causes an incorrect job submission.

fileUris

val fileUris: Output<List<String>>? = null

HCFS URIs of files to be placed in the working directory of each executor. Useful for naively parallel tasks.

loggingConfig

val loggingConfig: Output<WorkflowTemplateJobSparkRJobLoggingConfigArgs>? = null

The runtime log config for job execution.

mainRFileUri

val mainRFileUri: Output<String>

Required. The HCFS URI of the main R file to use as the driver. Must be a .R file.

properties

val properties: Output<Map<String, String>>? = null

A mapping of property names to values, used to configure SparkR. Properties that conflict with values set by the Dataproc API may be overwritten. Can include properties set in /etc/spark/conf/spark-defaults.conf and classes in user code.