// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::start_job_run::_start_job_run_output::StartJobRunOutputBuilder;

pub use crate::operation::start_job_run::_start_job_run_input::StartJobRunInputBuilder;

impl StartJobRunInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::start_job_run::StartJobRunOutput,
        ::aws_smithy_http::result::SdkError<
            crate::operation::start_job_run::StartJobRunError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.start_job_run();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `StartJobRun`.
///
/// <p>Starts a job run using a job definition.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct StartJobRunFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::start_job_run::builders::StartJobRunInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl StartJobRunFluentBuilder {
    /// Creates a new `StartJobRun`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the StartJobRun as a reference.
    pub fn as_input(&self) -> &crate::operation::start_job_run::builders::StartJobRunInputBuilder {
        &self.inner
    }
    /// 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](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::start_job_run::StartJobRunOutput,
        ::aws_smithy_http::result::SdkError<
            crate::operation::start_job_run::StartJobRunError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self.inner.build().map_err(::aws_smithy_http::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::start_job_run::StartJobRun::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::start_job_run::StartJobRun::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being
    /// sent.
    // TODO(enableNewSmithyRuntimeCleanup): Remove `async` and `Result` once we switch to orchestrator
    pub async fn customize(
        self,
    ) -> ::std::result::Result<
        crate::client::customize::orchestrator::CustomizableOperation<
            crate::operation::start_job_run::StartJobRunOutput,
            crate::operation::start_job_run::StartJobRunError,
        >,
        ::aws_smithy_http::result::SdkError<crate::operation::start_job_run::StartJobRunError>,
    > {
        ::std::result::Result::Ok(crate::client::customize::orchestrator::CustomizableOperation {
            customizable_send: ::std::boxed::Box::new(move |config_override| {
                ::std::boxed::Box::pin(async { self.config_override(config_override).send().await })
            }),
            config_override: None,
            interceptors: vec![],
            runtime_plugins: vec![],
        })
    }
    pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
        self.set_config_override(Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The name of the job definition to use.</p>
    pub fn job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.job_name(input.into());
        self
    }
    /// <p>The name of the job definition to use.</p>
    pub fn set_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_job_name(input);
        self
    }
    /// <p>The name of the job definition to use.</p>
    pub fn get_job_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_job_name()
    }
    /// <p>The ID of a previous <code>JobRun</code> to retry.</p>
    pub fn job_run_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.job_run_id(input.into());
        self
    }
    /// <p>The ID of a previous <code>JobRun</code> to retry.</p>
    pub fn set_job_run_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_job_run_id(input);
        self
    }
    /// <p>The ID of a previous <code>JobRun</code> to retry.</p>
    pub fn get_job_run_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_job_run_id()
    }
    /// Adds a key-value pair to `Arguments`.
    ///
    /// To override the contents of this collection use [`set_arguments`](Self::set_arguments).
    ///
    /// <p>The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.</p>
    /// <p>You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.</p>
    /// <p>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. </p>
    /// <p>For information about how to specify and consume your own Job arguments, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-calling.html">Calling Glue APIs in Python</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Spark jobs, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-glue-arguments.html">Special Parameters Used by Glue</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Ray jobs, see <a href="https://docs.aws.amazon.com/glue/latest/dg/author-job-ray-job-parameters.html">Using job parameters in Ray jobs</a> in the developer guide.</p>
    pub fn arguments(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.arguments(k.into(), v.into());
        self
    }
    /// <p>The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.</p>
    /// <p>You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.</p>
    /// <p>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. </p>
    /// <p>For information about how to specify and consume your own Job arguments, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-calling.html">Calling Glue APIs in Python</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Spark jobs, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-glue-arguments.html">Special Parameters Used by Glue</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Ray jobs, see <a href="https://docs.aws.amazon.com/glue/latest/dg/author-job-ray-job-parameters.html">Using job parameters in Ray jobs</a> in the developer guide.</p>
    pub fn set_arguments(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_arguments(input);
        self
    }
    /// <p>The job arguments associated with this run. For this job run, they replace the default arguments set in the job definition itself.</p>
    /// <p>You can specify arguments here that your own job-execution script consumes, as well as arguments that Glue itself consumes.</p>
    /// <p>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. </p>
    /// <p>For information about how to specify and consume your own Job arguments, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-calling.html">Calling Glue APIs in Python</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Spark jobs, see the <a href="https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-glue-arguments.html">Special Parameters Used by Glue</a> topic in the developer guide.</p>
    /// <p>For information about the arguments you can provide to this field when configuring Ray jobs, see <a href="https://docs.aws.amazon.com/glue/latest/dg/author-job-ray-job-parameters.html">Using job parameters in Ray jobs</a> in the developer guide.</p>
    pub fn get_arguments(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_arguments()
    }
    /// <p>This field is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/">Glue pricing page</a>.</p>
    #[deprecated(note = "This property is deprecated, use MaxCapacity instead.")]
    pub fn allocated_capacity(mut self, input: i32) -> Self {
        self.inner = self.inner.allocated_capacity(input);
        self
    }
    /// <p>This field is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/">Glue pricing page</a>.</p>
    #[deprecated(note = "This property is deprecated, use MaxCapacity instead.")]
    pub fn set_allocated_capacity(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_allocated_capacity(input);
        self
    }
    /// <p>This field is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/">Glue pricing page</a>.</p>
    #[deprecated(note = "This property is deprecated, use MaxCapacity instead.")]
    pub fn get_allocated_capacity(&self) -> &::std::option::Option<i32> {
        self.inner.get_allocated_capacity()
    }
    /// <p>The <code>JobRun</code> timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters <code>TIMEOUT</code> status. This value overrides the timeout value set in the parent job.</p>
    /// <p>Streaming jobs do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours).</p>
    pub fn timeout(mut self, input: i32) -> Self {
        self.inner = self.inner.timeout(input);
        self
    }
    /// <p>The <code>JobRun</code> timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters <code>TIMEOUT</code> status. This value overrides the timeout value set in the parent job.</p>
    /// <p>Streaming jobs do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours).</p>
    pub fn set_timeout(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_timeout(input);
        self
    }
    /// <p>The <code>JobRun</code> timeout in minutes. This is the maximum time that a job run can consume resources before it is terminated and enters <code>TIMEOUT</code> status. This value overrides the timeout value set in the parent job.</p>
    /// <p>Streaming jobs do not have a timeout. The default for non-streaming jobs is 2,880 minutes (48 hours).</p>
    pub fn get_timeout(&self) -> &::std::option::Option<i32> {
        self.inner.get_timeout()
    }
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/"> Glue pricing page</a>.</p>
    /// <p>For Glue version 2.0+ jobs, you cannot specify a <code>Maximum capacity</code>. Instead, you should specify a <code>Worker type</code> and the <code>Number of workers</code>.</p>
    /// <p>Do not set <code>MaxCapacity</code> if using <code>WorkerType</code> and <code>NumberOfWorkers</code>.</p>
    /// <p>The value that can be allocated for <code>MaxCapacity</code> depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:</p>
    /// <ul>
    /// <li> <p>When you specify a Python shell job (<code>JobCommand.Name</code>="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.</p> </li>
    /// <li> <p>When you specify an Apache Spark ETL job (<code>JobCommand.Name</code>="glueetl") or Apache Spark streaming ETL job (<code>JobCommand.Name</code>="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.</p> </li>
    /// </ul>
    pub fn max_capacity(mut self, input: f64) -> Self {
        self.inner = self.inner.max_capacity(input);
        self
    }
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/"> Glue pricing page</a>.</p>
    /// <p>For Glue version 2.0+ jobs, you cannot specify a <code>Maximum capacity</code>. Instead, you should specify a <code>Worker type</code> and the <code>Number of workers</code>.</p>
    /// <p>Do not set <code>MaxCapacity</code> if using <code>WorkerType</code> and <code>NumberOfWorkers</code>.</p>
    /// <p>The value that can be allocated for <code>MaxCapacity</code> depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:</p>
    /// <ul>
    /// <li> <p>When you specify a Python shell job (<code>JobCommand.Name</code>="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.</p> </li>
    /// <li> <p>When you specify an Apache Spark ETL job (<code>JobCommand.Name</code>="glueetl") or Apache Spark streaming ETL job (<code>JobCommand.Name</code>="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.</p> </li>
    /// </ul>
    pub fn set_max_capacity(mut self, input: ::std::option::Option<f64>) -> Self {
        self.inner = self.inner.set_max_capacity(input);
        self
    }
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/"> Glue pricing page</a>.</p>
    /// <p>For Glue version 2.0+ jobs, you cannot specify a <code>Maximum capacity</code>. Instead, you should specify a <code>Worker type</code> and the <code>Number of workers</code>.</p>
    /// <p>Do not set <code>MaxCapacity</code> if using <code>WorkerType</code> and <code>NumberOfWorkers</code>.</p>
    /// <p>The value that can be allocated for <code>MaxCapacity</code> depends on whether you are running a Python shell job, an Apache Spark ETL job, or an Apache Spark streaming ETL job:</p>
    /// <ul>
    /// <li> <p>When you specify a Python shell job (<code>JobCommand.Name</code>="pythonshell"), you can allocate either 0.0625 or 1 DPU. The default is 0.0625 DPU.</p> </li>
    /// <li> <p>When you specify an Apache Spark ETL job (<code>JobCommand.Name</code>="glueetl") or Apache Spark streaming ETL job (<code>JobCommand.Name</code>="gluestreaming"), you can allocate from 2 to 100 DPUs. The default is 10 DPUs. This job type cannot have a fractional DPU allocation.</p> </li>
    /// </ul>
    pub fn get_max_capacity(&self) -> &::std::option::Option<f64> {
        self.inner.get_max_capacity()
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job run.</p>
    pub fn security_configuration(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.security_configuration(input.into());
        self
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job run.</p>
    pub fn set_security_configuration(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_security_configuration(input);
        self
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job run.</p>
    pub fn get_security_configuration(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_security_configuration()
    }
    /// <p>Specifies configuration properties of a job run notification.</p>
    pub fn notification_property(mut self, input: crate::types::NotificationProperty) -> Self {
        self.inner = self.inner.notification_property(input);
        self
    }
    /// <p>Specifies configuration properties of a job run notification.</p>
    pub fn set_notification_property(mut self, input: ::std::option::Option<crate::types::NotificationProperty>) -> Self {
        self.inner = self.inner.set_notification_property(input);
        self
    }
    /// <p>Specifies configuration properties of a job run notification.</p>
    pub fn get_notification_property(&self) -> &::std::option::Option<crate::types::NotificationProperty> {
        self.inner.get_notification_property()
    }
    /// <p>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.</p>
    /// <ul>
    /// <li> <p>For the <code>G.1X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.2X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.4X</code> 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).</p> </li>
    /// <li> <p>For the <code>G.8X</code> 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 <code>G.4X</code> worker type.</p> </li>
    /// <li> <p>For the <code>G.025X</code> 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.</p> </li>
    /// <li> <p>For the <code>Z.2X</code> 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.</p> </li>
    /// </ul>
    pub fn worker_type(mut self, input: crate::types::WorkerType) -> Self {
        self.inner = self.inner.worker_type(input);
        self
    }
    /// <p>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.</p>
    /// <ul>
    /// <li> <p>For the <code>G.1X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.2X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.4X</code> 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).</p> </li>
    /// <li> <p>For the <code>G.8X</code> 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 <code>G.4X</code> worker type.</p> </li>
    /// <li> <p>For the <code>G.025X</code> 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.</p> </li>
    /// <li> <p>For the <code>Z.2X</code> 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.</p> </li>
    /// </ul>
    pub fn set_worker_type(mut self, input: ::std::option::Option<crate::types::WorkerType>) -> Self {
        self.inner = self.inner.set_worker_type(input);
        self
    }
    /// <p>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.</p>
    /// <ul>
    /// <li> <p>For the <code>G.1X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.2X</code> 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.</p> </li>
    /// <li> <p>For the <code>G.4X</code> 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).</p> </li>
    /// <li> <p>For the <code>G.8X</code> 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 <code>G.4X</code> worker type.</p> </li>
    /// <li> <p>For the <code>G.025X</code> 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.</p> </li>
    /// <li> <p>For the <code>Z.2X</code> 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.</p> </li>
    /// </ul>
    pub fn get_worker_type(&self) -> &::std::option::Option<crate::types::WorkerType> {
        self.inner.get_worker_type()
    }
    /// <p>The number of workers of a defined <code>workerType</code> that are allocated when a job runs.</p>
    pub fn number_of_workers(mut self, input: i32) -> Self {
        self.inner = self.inner.number_of_workers(input);
        self
    }
    /// <p>The number of workers of a defined <code>workerType</code> that are allocated when a job runs.</p>
    pub fn set_number_of_workers(mut self, input: ::std::option::Option<i32>) -> Self {
        self.inner = self.inner.set_number_of_workers(input);
        self
    }
    /// <p>The number of workers of a defined <code>workerType</code> that are allocated when a job runs.</p>
    pub fn get_number_of_workers(&self) -> &::std::option::Option<i32> {
        self.inner.get_number_of_workers()
    }
    /// <p>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.</p>
    /// <p>The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary. </p>
    /// <p>Only jobs with Glue version 3.0 and above and command type <code>glueetl</code> will be allowed to set <code>ExecutionClass</code> to <code>FLEX</code>. The flexible execution class is available for Spark jobs.</p>
    pub fn execution_class(mut self, input: crate::types::ExecutionClass) -> Self {
        self.inner = self.inner.execution_class(input);
        self
    }
    /// <p>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.</p>
    /// <p>The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary. </p>
    /// <p>Only jobs with Glue version 3.0 and above and command type <code>glueetl</code> will be allowed to set <code>ExecutionClass</code> to <code>FLEX</code>. The flexible execution class is available for Spark jobs.</p>
    pub fn set_execution_class(mut self, input: ::std::option::Option<crate::types::ExecutionClass>) -> Self {
        self.inner = self.inner.set_execution_class(input);
        self
    }
    /// <p>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.</p>
    /// <p>The flexible execution class is appropriate for time-insensitive jobs whose start and completion times may vary. </p>
    /// <p>Only jobs with Glue version 3.0 and above and command type <code>glueetl</code> will be allowed to set <code>ExecutionClass</code> to <code>FLEX</code>. The flexible execution class is available for Spark jobs.</p>
    pub fn get_execution_class(&self) -> &::std::option::Option<crate::types::ExecutionClass> {
        self.inner.get_execution_class()
    }
}