StartJobRunFluentBuilder

aws_sdk_glue::operation::start_job_run::builders

Struct StartJobRunFluentBuilder 

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pub struct StartJobRunFluentBuilder { /* private fields */ }
Expand description

Fluent builder constructing a request to StartJobRun.

Starts a job run using a job definition.

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impl StartJobRunFluentBuilder

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pub fn as_input(&self) -> &StartJobRunInputBuilder

Access the StartJobRun as a reference.

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pub async fn send( self, ) -> Result<StartJobRunOutput, SdkError<StartJobRunError, 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.

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pub fn customize( self, ) -> CustomizableOperation<StartJobRunOutput, StartJobRunError, Self>

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

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

The name of the job definition to use.

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

The name of the job definition to use.

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

The name of the job definition to use.

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pub fn job_run_queuing_enabled(self, input: bool) -> Self

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.

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pub fn set_job_run_queuing_enabled(self, input: Option<bool>) -> Self

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.

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pub fn get_job_run_queuing_enabled(&self) -> &Option<bool>

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.

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

The ID of a previous JobRun to retry.

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

The ID of a previous JobRun to retry.

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

The ID of a previous JobRun to retry.

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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.

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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.

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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.

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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) 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.

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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) 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.

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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) 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.

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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.

Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.

When the value is left blank, the timeout is defaulted to 2880 minutes.

Any existing Glue jobs that had a timeout value greater than 7 days will be defaulted to 7 days. For instance if you have specified a timeout of 20 days for a batch job, it will be stopped on the 7th day.

For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.

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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.

Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.

When the value is left blank, the timeout is defaulted to 2880 minutes.

Any existing Glue jobs that had a timeout value greater than 7 days will be defaulted to 7 days. For instance if you have specified a timeout of 20 days for a batch job, it will be stopped on the 7th day.

For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.

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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.

Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.

When the value is left blank, the timeout is defaulted to 2880 minutes.

Any existing Glue jobs that had a timeout value greater than 7 days will be defaulted to 7 days. For instance if you have specified a timeout of 20 days for a batch job, it will be stopped on the 7th day.

For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.

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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.

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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.

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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.

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

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

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

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

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

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

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

Specifies configuration properties of a job run notification.

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

Specifies configuration properties of a job run notification.

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

Specifies configuration properties of a job run notification.

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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 94GB disk, 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 138GB disk, 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, 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, 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, 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 or later streaming jobs.

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

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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 94GB disk, 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 138GB disk, 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, 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, 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, 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 or later streaming jobs.

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

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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 94GB disk, 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 138GB disk, 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, 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, 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, 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 or later streaming jobs.

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

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

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

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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.

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

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

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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.

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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.

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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.

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

This inline session policy to the StartJobRun API allows you to dynamically restrict the permissions of the specified execution role for the scope of the job, without requiring the creation of additional IAM roles.

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

This inline session policy to the StartJobRun API allows you to dynamically restrict the permissions of the specified execution role for the scope of the job, without requiring the creation of additional IAM roles.

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

This inline session policy to the StartJobRun API allows you to dynamically restrict the permissions of the specified execution role for the scope of the job, without requiring the creation of additional IAM roles.

Trait Implementations§

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impl Clone for StartJobRunFluentBuilder

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fn clone(&self) -> StartJobRunFluentBuilder

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for StartJobRunFluentBuilder

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

Formats the value using the given formatter. Read more

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Set background color to red using fg():

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painted.bg(Color::Red);

Set background color to red using on_red().

use yansi::Paint;

painted.on_red();
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fn on_primary(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Primary].

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println!("{}", value.on_primary());
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fn on_fixed(&self, color: u8) -> Painted<&T>

Returns self with the bg() set to [Color :: Fixed].

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println!("{}", value.on_fixed(color));
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fn on_rgb(&self, r: u8, g: u8, b: u8) -> Painted<&T>

Returns self with the bg() set to [Color :: Rgb].

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println!("{}", value.on_rgb(r, g, b));
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fn on_black(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Black].

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println!("{}", value.on_black());
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fn on_red(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Red].

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println!("{}", value.on_red());
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fn on_green(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Green].

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println!("{}", value.on_green());
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fn on_yellow(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Yellow].

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println!("{}", value.on_yellow());
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fn on_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Blue].

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println!("{}", value.on_blue());
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fn on_magenta(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Magenta].

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println!("{}", value.on_magenta());
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fn on_cyan(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Cyan].

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println!("{}", value.on_cyan());
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fn on_white(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: White].

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println!("{}", value.on_white());
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fn on_bright_black(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlack].

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println!("{}", value.on_bright_black());
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fn on_bright_red(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightRed].

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println!("{}", value.on_bright_red());
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fn on_bright_green(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightGreen].

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println!("{}", value.on_bright_green());
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fn on_bright_yellow(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightYellow].

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println!("{}", value.on_bright_yellow());
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fn on_bright_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlue].

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println!("{}", value.on_bright_blue());
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fn on_bright_magenta(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightMagenta].

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println!("{}", value.on_bright_magenta());
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fn on_bright_cyan(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightCyan].

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println!("{}", value.on_bright_cyan());
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fn on_bright_white(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightWhite].

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println!("{}", value.on_bright_white());
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fn attr(&self, value: Attribute) -> Painted<&T>

Enables the styling Attribute value.

This method should be used rarely. Instead, prefer to use attribute-specific builder methods like bold() and underline(), which have the same functionality but are pithier.

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Make text bold using attr():

use yansi::{Paint, Attribute};

painted.attr(Attribute::Bold);

Make text bold using using bold().

use yansi::Paint;

painted.bold();
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fn bold(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Bold].

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println!("{}", value.bold());
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fn dim(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Dim].

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println!("{}", value.dim());
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fn italic(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Italic].

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println!("{}", value.italic());
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fn underline(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Underline].

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println!("{}", value.underline());

Returns self with the attr() set to [Attribute :: Blink].

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println!("{}", value.blink());

Returns self with the attr() set to [Attribute :: RapidBlink].

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println!("{}", value.rapid_blink());
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fn invert(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Invert].

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println!("{}", value.invert());
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fn conceal(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Conceal].

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println!("{}", value.conceal());
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fn strike(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Strike].

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println!("{}", value.strike());
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fn quirk(&self, value: Quirk) -> Painted<&T>

Enables the yansi Quirk value.

This method should be used rarely. Instead, prefer to use quirk-specific builder methods like mask() and wrap(), which have the same functionality but are pithier.

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Enable wrapping using .quirk():

use yansi::{Paint, Quirk};

painted.quirk(Quirk::Wrap);

Enable wrapping using wrap().

use yansi::Paint;

painted.wrap();
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fn mask(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Mask].

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println!("{}", value.mask());
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fn wrap(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Wrap].

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println!("{}", value.wrap());
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fn linger(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Linger].

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println!("{}", value.linger());
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fn clear(&self) -> Painted<&T>

👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

Returns self with the quirk() set to [Quirk :: Clear].

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println!("{}", value.clear());
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fn resetting(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Resetting].

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println!("{}", value.resetting());
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fn bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Bright].

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println!("{}", value.bright());
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fn on_bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: OnBright].

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println!("{}", value.on_bright());
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fn whenever(&self, value: Condition) -> Painted<&T>

Conditionally enable styling based on whether the Condition value applies. Replaces any previous condition.

See the crate level docs for more details.

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Enable styling painted only when both stdout and stderr are TTYs:

use yansi::{Paint, Condition};

painted.red().on_yellow().whenever(Condition::STDOUTERR_ARE_TTY);
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fn new(self) -> Painted<Self>
where Self: Sized,

Create a new Painted with a default Style. Read more
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fn paint<S>(&self, style: S) -> Painted<&Self>
where S: Into<Style>,

Apply a style wholesale to self. Any previous style is replaced. Read more
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impl<T> Same for T

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type Output = T

Should always be Self
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impl<T> ToOwned for T
where T: Clone,

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<T> WithSubscriber for T

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fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

Attaches the provided Subscriber to this type, returning a WithDispatch wrapper. Read more
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fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more