Struct aws_sdk_glue::types::builders::JobRunBuilder

#[non_exhaustive]
pub struct JobRunBuilder { /* private fields */ }

A builder for JobRun.

Implementations

impl JobRunBuilder

pub fn id(self, input: impl Into<String>) -> Self

The ID of this job run.

pub fn set_id(self, input: Option<String>) -> Self

The ID of this job run.

pub fn get_id(&self) -> &Option<String>

The ID of this job run.

pub fn attempt(self, input: i32) -> Self

The number of the attempt to run this job.

pub fn set_attempt(self, input: Option<i32>) -> Self

The number of the attempt to run this job.

pub fn get_attempt(&self) -> &Option<i32>

The number of the attempt to run this job.

pub fn previous_run_id(self, input: impl Into<String>) -> Self

The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action.

pub fn set_previous_run_id(self, input: Option<String>) -> Self

The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action.

pub fn get_previous_run_id(&self) -> &Option<String>

The ID of the previous run of this job. For example, the JobRunId specified in the StartJobRun action.

pub fn trigger_name(self, input: impl Into<String>) -> Self

The name of the trigger that started this job run.

pub fn set_trigger_name(self, input: Option<String>) -> Self

The name of the trigger that started this job run.

pub fn get_trigger_name(&self) -> &Option<String>

The name of the trigger that started this job run.

pub fn job_name(self, input: impl Into<String>) -> Self

The name of the job definition being used in this run.

pub fn set_job_name(self, input: Option<String>) -> Self

The name of the job definition being used in this run.

pub fn get_job_name(&self) -> &Option<String>

The name of the job definition being used in this run.

pub fn job_mode(self, input: JobMode) -> Self

A mode that describes how a job was created. Valid values are:

• SCRIPT - The job was created using the Glue Studio script editor.

• VISUAL - The job was created using the Glue Studio visual editor.

• NOTEBOOK - The job was created using an interactive sessions notebook.

When the JobMode field is missing or null, SCRIPT is assigned as the default value.

pub fn set_job_mode(self, input: Option<JobMode>) -> Self

A mode that describes how a job was created. Valid values are:

• SCRIPT - The job was created using the Glue Studio script editor.

• VISUAL - The job was created using the Glue Studio visual editor.

• NOTEBOOK - The job was created using an interactive sessions notebook.

When the JobMode field is missing or null, SCRIPT is assigned as the default value.

pub fn get_job_mode(&self) -> &Option<JobMode>

A mode that describes how a job was created. Valid values are:

• SCRIPT - The job was created using the Glue Studio script editor.

• VISUAL - The job was created using the Glue Studio visual editor.

• NOTEBOOK - The job was created using an interactive sessions notebook.

When the JobMode field is missing or null, SCRIPT is assigned as the default value.

pub fn started_on(self, input: DateTime) -> Self

The date and time at which this job run was started.

pub fn set_started_on(self, input: Option<DateTime>) -> Self

The date and time at which this job run was started.

pub fn get_started_on(&self) -> &Option<DateTime>

The date and time at which this job run was started.

pub fn last_modified_on(self, input: DateTime) -> Self

The last time that this job run was modified.

pub fn set_last_modified_on(self, input: Option<DateTime>) -> Self

The last time that this job run was modified.

pub fn get_last_modified_on(&self) -> &Option<DateTime>

The last time that this job run was modified.

pub fn completed_on(self, input: DateTime) -> Self

The date and time that this job run completed.

pub fn set_completed_on(self, input: Option<DateTime>) -> Self

The date and time that this job run completed.

pub fn get_completed_on(&self) -> &Option<DateTime>

The date and time that this job run completed.

pub fn job_run_state(self, input: JobRunState) -> Self

The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see Glue Job Run Statuses.

pub fn set_job_run_state(self, input: Option<JobRunState>) -> Self

The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see Glue Job Run Statuses.

pub fn get_job_run_state(&self) -> &Option<JobRunState>

The current state of the job run. For more information about the statuses of jobs that have terminated abnormally, see Glue Job Run Statuses.

pub fn arguments(self, k: impl Into<String>, v: impl Into<String>) -> Self

Adds a key-value pair to arguments.

To override the contents of this collection use set_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.

pub fn set_arguments(self, input: Option<HashMap<String, String>>) -> Self

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.

pub fn get_arguments(&self) -> &Option<HashMap<String, String>>

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.

pub fn error_message(self, input: impl Into<String>) -> Self

An error message associated with this job run.

pub fn set_error_message(self, input: Option<String>) -> Self

An error message associated with this job run.

pub fn get_error_message(&self) -> &Option<String>

An error message associated with this job run.

pub fn predecessor_runs(self, input: Predecessor) -> Self

Appends an item to predecessor_runs.

To override the contents of this collection use set_predecessor_runs.

A list of predecessors to this job run.

pub fn set_predecessor_runs(self, input: Option<Vec<Predecessor>>) -> Self

A list of predecessors to this job run.

pub fn get_predecessor_runs(&self) -> &Option<Vec<Predecessor>>

A list of predecessors to this job run.

pub fn allocated_capacity(self, input: i32) -> Self

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; 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.

pub fn set_allocated_capacity(self, input: Option<i32>) -> Self

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; 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.

pub fn get_allocated_capacity(&self) -> &Option<i32>

👎Deprecated: This property is deprecated, use MaxCapacity instead.

This field is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) allocated to this JobRun. From 2 to 100 DPUs can be allocated; 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.

pub fn execution_time(self, input: i32) -> Self

The amount of time (in seconds) that the job run consumed resources.

pub fn set_execution_time(self, input: Option<i32>) -> Self

The amount of time (in seconds) that the job run consumed resources.

pub fn get_execution_time(&self) -> &Option<i32>

The amount of time (in seconds) that the job run consumed resources.

pub fn timeout(self, input: i32) -> Self

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.

pub fn set_timeout(self, input: Option<i32>) -> Self

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.

pub fn get_timeout(&self) -> &Option<i32>

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.

pub fn max_capacity(self, input: f64) -> Self

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.

pub fn set_max_capacity(self, input: Option<f64>) -> Self

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.

pub fn get_max_capacity(&self) -> &Option<f64>

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.

pub fn worker_type(self, input: WorkerType) -> Self

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.

pub fn set_worker_type(self, input: Option<WorkerType>) -> Self

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.

pub fn get_worker_type(&self) -> &Option<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.

pub fn number_of_workers(self, input: i32) -> Self

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

pub fn set_number_of_workers(self, input: Option<i32>) -> Self

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

pub fn get_number_of_workers(&self) -> &Option<i32>

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

pub fn security_configuration(self, input: impl Into<String>) -> Self

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

pub fn set_security_configuration(self, input: Option<String>) -> Self

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

pub fn get_security_configuration(&self) -> &Option<String>

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

pub fn log_group_name(self, input: impl Into<String>) -> Self

The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS. This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is used to encrypt the log group.

pub fn set_log_group_name(self, input: Option<String>) -> Self

The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS. This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is used to encrypt the log group.

pub fn get_log_group_name(&self) -> &Option<String>

The name of the log group for secure logging that can be server-side encrypted in Amazon CloudWatch using KMS. This name can be /aws-glue/jobs/, in which case the default encryption is NONE. If you add a role name and SecurityConfiguration name (in other words, /aws-glue/jobs-yourRoleName-yourSecurityConfigurationName/), then that security configuration is used to encrypt the log group.

pub fn notification_property(self, input: NotificationProperty) -> Self

Specifies configuration properties of a job run notification.

pub fn set_notification_property( self, input: Option<NotificationProperty>, ) -> Self

Specifies configuration properties of a job run notification.

pub fn get_notification_property(&self) -> &Option<NotificationProperty>

Specifies configuration properties of a job run notification.

pub fn glue_version(self, input: impl Into<String>) -> Self

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn set_glue_version(self, input: Option<String>) -> Self

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn get_glue_version(&self) -> &Option<String>

In Spark jobs, GlueVersion determines the versions of Apache Spark and Python that Glue available in a job. The Python version indicates the version supported for jobs of type Spark.

Ray jobs should set GlueVersion to 4.0 or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the Runtime parameter of the Job command.

For more information about the available Glue versions and corresponding Spark and Python versions, see Glue version in the developer guide.

Jobs that are created without specifying a Glue version default to Glue 0.9.

pub fn dpu_seconds(self, input: f64) -> Self

This field can be set for either job runs with execution class FLEX or when Auto Scaling is enabled, and represents the total time each executor ran during the lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X, or 0.25 for G.025X workers). This value may be different than the executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible that the value of DPUSeconds is less than executionEngineRuntime * MaxCapacity.

pub fn set_dpu_seconds(self, input: Option<f64>) -> Self

This field can be set for either job runs with execution class FLEX or when Auto Scaling is enabled, and represents the total time each executor ran during the lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X, or 0.25 for G.025X workers). This value may be different than the executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible that the value of DPUSeconds is less than executionEngineRuntime * MaxCapacity.

pub fn get_dpu_seconds(&self) -> &Option<f64>

This field can be set for either job runs with execution class FLEX or when Auto Scaling is enabled, and represents the total time each executor ran during the lifecycle of a job run in seconds, multiplied by a DPU factor (1 for G.1X, 2 for G.2X, or 0.25 for G.025X workers). This value may be different than the executionEngineRuntime * MaxCapacity as in the case of Auto Scaling jobs, as the number of executors running at a given time may be less than the MaxCapacity. Therefore, it is possible that the value of DPUSeconds is less than executionEngineRuntime * MaxCapacity.

pub fn execution_class(self, input: ExecutionClass) -> Self

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.

pub fn set_execution_class(self, input: Option<ExecutionClass>) -> Self

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.

pub fn get_execution_class(&self) -> &Option<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.

pub fn maintenance_window(self, input: impl Into<String>) -> Self

This field specifies a day of the week and hour for a maintenance window for streaming jobs. Glue periodically performs maintenance activities. During these maintenance windows, Glue will need to restart your streaming jobs.

Glue will restart the job within 3 hours of the specified maintenance window. For instance, if you set up the maintenance window for Monday at 10:00AM GMT, your jobs will be restarted between 10:00AM GMT to 1:00PM GMT.

pub fn set_maintenance_window(self, input: Option<String>) -> Self

This field specifies a day of the week and hour for a maintenance window for streaming jobs. Glue periodically performs maintenance activities. During these maintenance windows, Glue will need to restart your streaming jobs.

Glue will restart the job within 3 hours of the specified maintenance window. For instance, if you set up the maintenance window for Monday at 10:00AM GMT, your jobs will be restarted between 10:00AM GMT to 1:00PM GMT.

pub fn get_maintenance_window(&self) -> &Option<String>

This field specifies a day of the week and hour for a maintenance window for streaming jobs. Glue periodically performs maintenance activities. During these maintenance windows, Glue will need to restart your streaming jobs.

Glue will restart the job within 3 hours of the specified maintenance window. For instance, if you set up the maintenance window for Monday at 10:00AM GMT, your jobs will be restarted between 10:00AM GMT to 1:00PM GMT.

pub fn build(self) -> JobRun

Consumes the builder and constructs a JobRun.

Trait Implementations

impl Clone for JobRunBuilder

fn clone(&self) -> JobRunBuilder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for JobRunBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for JobRunBuilder

fn default() -> JobRunBuilder

Returns the “default value” for a type.

impl PartialEq for JobRunBuilder

fn eq(&self, other: &JobRunBuilder) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for JobRunBuilder