Struct aws_sdk_glue::operation::create_job::builders::CreateJobFluentBuilder

pub struct CreateJobFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateJob.

Creates a new job definition.

Implementations

impl CreateJobFluentBuilder

pub fn as_input(&self) -> &CreateJobInputBuilder

Access the CreateJob as a reference.

pub async fn send( self, ) -> Result<CreateJobOutput, SdkError<CreateJobError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self, ) -> CustomizableOperation<CreateJobOutput, CreateJobError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

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

The name you assign to this job definition. It must be unique in your account.

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

The name you assign to this job definition. It must be unique in your account.

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

The name you assign to this job definition. It must be unique in your account.

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 description(self, input: impl Into<String>) -> Self

Description of the job being defined.

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

Description of the job being defined.

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

Description of the job being defined.

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

This field is reserved for future use.

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

This field is reserved for future use.

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

This field is reserved for future use.

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

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

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

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

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

The name or Amazon Resource Name (ARN) of the IAM role associated with this job.

pub fn execution_property(self, input: ExecutionProperty) -> Self

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn set_execution_property(self, input: Option<ExecutionProperty>) -> Self

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn get_execution_property(&self) -> &Option<ExecutionProperty>

An ExecutionProperty specifying the maximum number of concurrent runs allowed for this job.

pub fn command(self, input: JobCommand) -> Self

The JobCommand that runs this job.

pub fn set_command(self, input: Option<JobCommand>) -> Self

The JobCommand that runs this job.

pub fn get_command(&self) -> &Option<JobCommand>

The JobCommand that runs this job.

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

Adds a key-value pair to DefaultArguments.

To override the contents of this collection use set_default_arguments.

The default arguments for every run of this job, specified as name-value pairs.

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_default_arguments( self, input: Option<HashMap<String, String>>, ) -> Self

The default arguments for every run of this job, specified as name-value pairs.

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_default_arguments(&self) -> &Option<HashMap<String, String>>

The default arguments for every run of this job, specified as name-value pairs.

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 non_overridable_arguments( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to NonOverridableArguments.

To override the contents of this collection use set_non_overridable_arguments.

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

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

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

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

Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.

pub fn connections(self, input: ConnectionsList) -> Self

The connections used for this job.

pub fn set_connections(self, input: Option<ConnectionsList>) -> Self

The connections used for this job.

pub fn get_connections(&self) -> &Option<ConnectionsList>

The connections used for this job.

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

The maximum number of times to retry this job if it fails.

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

The maximum number of times to retry this job if it fails.

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

The maximum number of times to retry this job if it fails.

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

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

This parameter is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) to allocate to this Job. 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.

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

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

This parameter is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) to allocate to this Job. 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.

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

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

This parameter is deprecated. Use MaxCapacity instead.

The number of Glue data processing units (DPUs) to allocate to this Job. 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.

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

The job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours) for batch jobs.

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 job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours) for batch jobs.

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 job timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters TIMEOUT status. The default is 2,880 minutes (48 hours) for batch jobs.

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

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

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

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

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

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

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

Adds a key-value pair to Tags.

To override the contents of this collection use set_tags.

The tags to use with this job. You may use tags to limit access to the job. For more information about tags in Glue, see Amazon Web Services Tags in Glue in the developer guide.

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

The tags to use with this job. You may use tags to limit access to the job. For more information about tags in Glue, see Amazon Web Services Tags in Glue in the developer guide.

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

The tags to use with this job. You may use tags to limit access to the job. For more information about tags in Glue, see Amazon Web Services Tags in Glue in the developer guide.

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

Specifies configuration properties of a job notification.

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

Specifies configuration properties of a job notification.

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

Specifies configuration properties of a job 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 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 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 code_gen_configuration_nodes( self, k: impl Into<String>, v: CodeGenConfigurationNode, ) -> Self

Adds a key-value pair to CodeGenConfigurationNodes.

To override the contents of this collection use set_code_gen_configuration_nodes.

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

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

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

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

The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.

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 source_control_details(self, input: SourceControlDetails) -> Self

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

pub fn set_source_control_details( self, input: Option<SourceControlDetails>, ) -> Self

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

pub fn get_source_control_details(&self) -> &Option<SourceControlDetails>

The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.

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.

Trait Implementations

impl Clone for CreateJobFluentBuilder

fn clone(&self) -> CreateJobFluentBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateJobFluentBuilder

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

Formats the value using the given formatter.