Package software.amazon.awssdk.services.glue.model

Class StartJobRunRequest

    • Method Detail

      • jobName

        public final String jobName()

        The name of the job definition to use.

        Returns:
        The name of the job definition to use.

      • jobRunQueuingEnabled

        public final Boolean jobRunQueuingEnabled()

        Specifies whether job run queuing is enabled for the job run.

        A value of true means job run queuing is enabled for the job run. If false or not populated, the job run will not be considered for queueing.

        Returns:
        Specifies whether job run queuing is enabled for the job run.

        A value of true means job run queuing is enabled for the job run. If false or not populated, the job run will not be considered for queueing.

      • jobRunId

        public final String jobRunId()

        The ID of a previous JobRun to retry.

        Returns:
        The ID of a previous JobRun to retry.

      • hasArguments

        public final boolean hasArguments()
        For responses, this returns true if the service returned a value for the Arguments property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

      • arguments

        public final Map<String,​String> arguments()

        The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.

        You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

        Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

        For information about how to specify and consume your own Job arguments, see the Calling Glue APIs in Python topic in the developer guide.

        For information about the arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

        For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

        Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

        This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasArguments() method.

        Returns:
        The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.

        You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.

        Job arguments may be logged. Do not pass plaintext secrets as arguments. Retrieve secrets from a Glue Connection, Secrets Manager or other secret management mechanism if you intend to keep them within the Job.

        For information about how to specify and consume your own Job arguments, see the Calling Glue APIs in Python topic in the developer guide.

        For information about the arguments you can provide to this field when configuring Spark jobs, see the Special Parameters Used by Glue topic in the developer guide.

        For information about the arguments you can provide to this field when configuring Ray jobs, see Using job parameters in Ray jobs in the developer guide.

      • allocatedCapacity

        @Deprecated
public final Integer allocatedCapacity()
        Deprecated.
        This property is deprecated, use MaxCapacity instead.

        This field is deprecated. Use MaxCapacity instead.

        The number of Glue data processing units (DPUs) to allocate to this JobRun. You can allocate a minimum of 2 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

        Returns:
        This field is deprecated. Use MaxCapacity instead.

        The number of Glue data processing units (DPUs) to allocate to this JobRun. You can allocate a minimum of 2 DPUs; the default is 10. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

      • timeout

        public final Integer timeout()

        The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. This value overrides the timeout value set in the parent job.

        Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.

        Returns:
        The JobRun timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. This value overrides the timeout value set in the parent job.

        Streaming jobs must have timeout values less than 7 days or 10080 minutes. When the value is left blank, the job will be restarted after 7 days based if you have not setup a maintenance window. If you have setup maintenance window, it will be restarted during the maintenance window after 7 days.

      • maxCapacity

        public final Double maxCapacity()

        For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

        For Glue version 2.0+ jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

        Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

        The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:

        • When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

        • When you specify an Apache Spark ETL job (JobCommand.Name="glueetl") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

        Returns:
        For Glue version 1.0 or earlier jobs, using the standard worker type, the number of Glue data processing units (DPUs) that can be allocated when this job runs. A DPU is a relative measure of processing power that consists of 4 vCPUs of compute capacity and 16 GB of memory. For more information, see the Glue pricing page.

        For Glue version 2.0+ jobs, you cannot specify a Maximum capacity. Instead, you should specify a Worker type and the Number of workers.

        Do not set MaxCapacity if using WorkerType and NumberOfWorkers.

        The value that can be allocated for MaxCapacity depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:

        • When you specify a Python shell job (JobCommand.Name="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.

        • When you specify an Apache Spark ETL job (JobCommand.Name="glueetl") or Apache Spark streaming ETL job (JobCommand.Name="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.

      • securityConfiguration

        public final String securityConfiguration()

        The name of the SecurityConfiguration structure to be used with this job run.

        Returns:
        The name of the SecurityConfiguration structure to be used with this job run.

      • notificationProperty

        public final NotificationProperty notificationProperty()

        Specifies configuration properties of a job run notification.

        Returns:
        Specifies configuration properties of a job run notification.

      • workerType

        public final WorkerType workerType()

        The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

        • For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

        • For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

        • For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

        • For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

        If the service returns an enum value that is not available in the current SDK version, workerType will return WorkerType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from workerTypeAsString().

        Returns:
        The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

        • For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

        • For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

        • For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

        • For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

        See Also:
        WorkerType

      • workerTypeAsString

        public final String workerTypeAsString()

        The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

        • For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

        • For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

        • For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

        • For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

        If the service returns an enum value that is not available in the current SDK version, workerType will return WorkerType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from workerTypeAsString().

        Returns:
        The type of predefined worker that is allocated when a job runs. Accepts a value of G.1X, G.2X, G.4X, G.8X or G.025X for Spark jobs. Accepts the value Z.2X for Ray jobs.

        • For the G.1X worker type, each worker maps to 1 DPU (4 vCPUs, 16 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.2X worker type, each worker maps to 2 DPU (8 vCPUs, 32 GB of memory) with 128GB disk (approximately 77GB free), and provides 1 executor per worker. We recommend this worker type for workloads such as data transforms, joins, and queries, to offers a scalable and cost effective way to run most jobs.

        • For the G.4X worker type, each worker maps to 4 DPU (16 vCPUs, 64 GB of memory) with 256GB disk (approximately 235GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs in the following Amazon Web Services Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

        • For the G.8X worker type, each worker maps to 8 DPU (32 vCPUs, 128 GB of memory) with 512GB disk (approximately 487GB free), and provides 1 executor per worker. We recommend this worker type for jobs whose workloads contain your most demanding transforms, aggregations, joins, and queries. This worker type is available only for Glue version 3.0 or later Spark ETL jobs, in the same Amazon Web Services Regions as supported for the G.4X worker type.

        • For the G.025X worker type, each worker maps to 0.25 DPU (2 vCPUs, 4 GB of memory) with 84GB disk (approximately 34GB free), and provides 1 executor per worker. We recommend this worker type for low volume streaming jobs. This worker type is only available for Glue version 3.0 streaming jobs.

        • For the Z.2X worker type, each worker maps to 2 M-DPU (8vCPUs, 64 GB of memory) with 128 GB disk (approximately 120GB free), and provides up to 8 Ray workers based on the autoscaler.

        See Also:
        WorkerType

      • numberOfWorkers

        public final Integer numberOfWorkers()

        The number of workers of a defined workerType that are allocated when a job runs.

        Returns:
        The number of workers of a defined workerType that are allocated when a job runs.

      • executionClass

        public final ExecutionClass executionClass()

        Indicates whether the job is run with a standard or flexible execution class. The standard execution-class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

        The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

        Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

        If the service returns an enum value that is not available in the current SDK version, executionClass will return ExecutionClass.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from executionClassAsString().

        Returns:
        Indicates whether the job is run with a standard or flexible execution class. The standard execution-class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

        The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

        Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

        See Also:
        ExecutionClass

      • executionClassAsString

        public final String executionClassAsString()

        Indicates whether the job is run with a standard or flexible execution class. The standard execution-class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

        The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

        Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

        If the service returns an enum value that is not available in the current SDK version, executionClass will return ExecutionClass.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from executionClassAsString().

        Returns:
        Indicates whether the job is run with a standard or flexible execution class. The standard execution-class is ideal for time-sensitive workloads that require fast job startup and dedicated resources.

        The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary.

        Only jobs with Glue version 3.0 and above and command type glueetl will be allowed to set ExecutionClass to FLEX. The flexible execution class is available for Spark jobs.

        See Also:
        ExecutionClass

      • toString

        public final String toString()
        Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
        Overrides:
        toString in class Object