aws_sdk_glue/operation/create_job/

_create_job_input.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq)]
pub struct CreateJobInput {
    /// <p>The name you assign to this job definition. It must be unique in your account.</p>
    pub name: ::std::option::Option<::std::string::String>,
    /// <p>A mode that describes how a job was created. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>SCRIPT</code> - The job was created using the Glue Studio script editor.</p></li>
    /// <li>
    /// <p><code>VISUAL</code> - The job was created using the Glue Studio visual editor.</p></li>
    /// <li>
    /// <p><code>NOTEBOOK</code> - The job was created using an interactive sessions notebook.</p></li>
    /// </ul>
    /// <p>When the <code>JobMode</code> field is missing or null, <code>SCRIPT</code> is assigned as the default value.</p>
    pub job_mode: ::std::option::Option<crate::types::JobMode>,
    /// <p>Specifies whether job run queuing is enabled for the job runs for this job.</p>
    /// <p>A value of true means job run queuing is enabled for the job runs. If false or not populated, the job runs will not be considered for queueing.</p>
    /// <p>If this field does not match the value set in the job run, then the value from the job run field will be used.</p>
    pub job_run_queuing_enabled: ::std::option::Option<bool>,
    /// <p>Description of the job being defined.</p>
    pub description: ::std::option::Option<::std::string::String>,
    /// <p>This field is reserved for future use.</p>
    pub log_uri: ::std::option::Option<::std::string::String>,
    /// <p>The name or Amazon Resource Name (ARN) of the IAM role associated with this job.</p>
    pub role: ::std::option::Option<::std::string::String>,
    /// <p>An <code>ExecutionProperty</code> specifying the maximum number of concurrent runs allowed for this job.</p>
    pub execution_property: ::std::option::Option<crate::types::ExecutionProperty>,
    /// <p>The <code>JobCommand</code> that runs this job.</p>
    pub command: ::std::option::Option<crate::types::JobCommand>,
    /// <p>The default arguments for every run of this job, specified as name-value pairs.</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 default_arguments: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    /// <p>Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.</p>
    pub non_overridable_arguments: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    /// <p>The connections used for this job.</p>
    pub connections: ::std::option::Option<crate::types::ConnectionsList>,
    /// <p>The maximum number of times to retry this job if it fails.</p>
    pub max_retries: ::std::option::Option<i32>,
    /// <p>This parameter is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <a href="https://aws.amazon.com/glue/pricing/">Glue pricing page</a>.</p>
    #[deprecated(note = "This property is deprecated, use MaxCapacity instead.")]
    pub allocated_capacity: ::std::option::Option<i32>,
    /// <p>The job 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.</p>
    /// <p>Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.</p>
    /// <p>When the value is left blank, the timeout is defaulted to 2,880 minutes for Glue version 4.0 and earlier, or 480 minutes for Glue version 5.0 and later.</p>
    /// <p>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.</p>
    /// <p>For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.</p>
    pub timeout: ::std::option::Option<i32>,
    /// <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 max_capacity: ::std::option::Option<f64>,
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job.</p>
    pub security_configuration: ::std::option::Option<::std::string::String>,
    /// <p>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 <a href="https://docs.aws.amazon.com/glue/latest/dg/monitor-tags.html">Amazon Web Services Tags in Glue</a> in the developer guide.</p>
    pub tags: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    /// <p>Specifies configuration properties of a job notification.</p>
    pub notification_property: ::std::option::Option<crate::types::NotificationProperty>,
    /// <p>In Spark jobs, <code>GlueVersion</code> 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.</p>
    /// <p>Ray jobs should set <code>GlueVersion</code> to <code>4.0</code> or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the <code>Runtime</code> parameter of the Job command.</p>
    /// <p>For more information about the available Glue versions and corresponding Spark and Python versions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Glue version</a> in the developer guide.</p>
    /// <p>Jobs that are created without specifying a Glue version default to Glue 5.1.</p>
    pub glue_version: ::std::option::Option<::std::string::String>,
    /// <p>The number of workers of a defined <code>workerType</code> that are allocated when a job runs.</p>
    pub number_of_workers: ::std::option::Option<i32>,
    /// <p>The type of predefined worker that is allocated when a job runs. Accepts a value of G.025X, G.1X, G.2X, G.4X, G.8X, G.12X, G.16X, R.1X, R.2X, R.4X, or R.8X for Spark jobs. Accepts the value Z.2X for Ray jobs.</p>
    /// <ul>
    /// <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, 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.</p></li>
    /// <li>
    /// <p>For the <code>G.1X</code> 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.</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 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.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.12X</code> worker type, each worker maps to 12 DPU (48 vCPUs, 192 GB of memory) with 768GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.16X</code> worker type, each worker maps to 16 DPU (64 vCPUs, 256 GB of memory) with 1024GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.1X</code> worker type, each worker maps to 1 M-DPU (4 vCPUs, 32 GB of memory) with 94GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.2X</code> worker type, each worker maps to 2 M-DPU (8 vCPUs, 64 GB of memory) with 138GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.4X</code> worker type, each worker maps to 4 M-DPU (16 vCPUs, 128 GB of memory) with 256GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.8X</code> worker type, each worker maps to 8 M-DPU (32 vCPUs, 256 GB of memory) with 512GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, and provides up to 8 Ray workers based on the autoscaler.</p></li>
    /// </ul>
    pub worker_type: ::std::option::Option<crate::types::WorkerType>,
    /// <p>The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.</p>
    pub code_gen_configuration_nodes:
        ::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::CodeGenConfigurationNode>>,
    /// <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 execution_class: ::std::option::Option<crate::types::ExecutionClass>,
    /// <p>The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.</p>
    pub source_control_details: ::std::option::Option<crate::types::SourceControlDetails>,
    /// <p>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.</p>
    /// <p>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.</p>
    pub maintenance_window: ::std::option::Option<::std::string::String>,
}
impl CreateJobInput {
    /// <p>The name you assign to this job definition. It must be unique in your account.</p>
    pub fn name(&self) -> ::std::option::Option<&str> {
        self.name.as_deref()
    }
    /// <p>A mode that describes how a job was created. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>SCRIPT</code> - The job was created using the Glue Studio script editor.</p></li>
    /// <li>
    /// <p><code>VISUAL</code> - The job was created using the Glue Studio visual editor.</p></li>
    /// <li>
    /// <p><code>NOTEBOOK</code> - The job was created using an interactive sessions notebook.</p></li>
    /// </ul>
    /// <p>When the <code>JobMode</code> field is missing or null, <code>SCRIPT</code> is assigned as the default value.</p>
    pub fn job_mode(&self) -> ::std::option::Option<&crate::types::JobMode> {
        self.job_mode.as_ref()
    }
    /// <p>Specifies whether job run queuing is enabled for the job runs for this job.</p>
    /// <p>A value of true means job run queuing is enabled for the job runs. If false or not populated, the job runs will not be considered for queueing.</p>
    /// <p>If this field does not match the value set in the job run, then the value from the job run field will be used.</p>
    pub fn job_run_queuing_enabled(&self) -> ::std::option::Option<bool> {
        self.job_run_queuing_enabled
    }
    /// <p>Description of the job being defined.</p>
    pub fn description(&self) -> ::std::option::Option<&str> {
        self.description.as_deref()
    }
    /// <p>This field is reserved for future use.</p>
    pub fn log_uri(&self) -> ::std::option::Option<&str> {
        self.log_uri.as_deref()
    }
    /// <p>The name or Amazon Resource Name (ARN) of the IAM role associated with this job.</p>
    pub fn role(&self) -> ::std::option::Option<&str> {
        self.role.as_deref()
    }
    /// <p>An <code>ExecutionProperty</code> specifying the maximum number of concurrent runs allowed for this job.</p>
    pub fn execution_property(&self) -> ::std::option::Option<&crate::types::ExecutionProperty> {
        self.execution_property.as_ref()
    }
    /// <p>The <code>JobCommand</code> that runs this job.</p>
    pub fn command(&self) -> ::std::option::Option<&crate::types::JobCommand> {
        self.command.as_ref()
    }
    /// <p>The default arguments for every run of this job, specified as name-value pairs.</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 default_arguments(&self) -> ::std::option::Option<&::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.default_arguments.as_ref()
    }
    /// <p>Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.</p>
    pub fn non_overridable_arguments(&self) -> ::std::option::Option<&::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.non_overridable_arguments.as_ref()
    }
    /// <p>The connections used for this job.</p>
    pub fn connections(&self) -> ::std::option::Option<&crate::types::ConnectionsList> {
        self.connections.as_ref()
    }
    /// <p>The maximum number of times to retry this job if it fails.</p>
    pub fn max_retries(&self) -> ::std::option::Option<i32> {
        self.max_retries
    }
    /// <p>This parameter is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <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(&self) -> ::std::option::Option<i32> {
        self.allocated_capacity
    }
    /// <p>The job 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.</p>
    /// <p>Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.</p>
    /// <p>When the value is left blank, the timeout is defaulted to 2,880 minutes for Glue version 4.0 and earlier, or 480 minutes for Glue version 5.0 and later.</p>
    /// <p>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.</p>
    /// <p>For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.</p>
    pub fn timeout(&self) -> ::std::option::Option<i32> {
        self.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(&self) -> ::std::option::Option<f64> {
        self.max_capacity
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job.</p>
    pub fn security_configuration(&self) -> ::std::option::Option<&str> {
        self.security_configuration.as_deref()
    }
    /// <p>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 <a href="https://docs.aws.amazon.com/glue/latest/dg/monitor-tags.html">Amazon Web Services Tags in Glue</a> in the developer guide.</p>
    pub fn tags(&self) -> ::std::option::Option<&::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.tags.as_ref()
    }
    /// <p>Specifies configuration properties of a job notification.</p>
    pub fn notification_property(&self) -> ::std::option::Option<&crate::types::NotificationProperty> {
        self.notification_property.as_ref()
    }
    /// <p>In Spark jobs, <code>GlueVersion</code> 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.</p>
    /// <p>Ray jobs should set <code>GlueVersion</code> to <code>4.0</code> or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the <code>Runtime</code> parameter of the Job command.</p>
    /// <p>For more information about the available Glue versions and corresponding Spark and Python versions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Glue version</a> in the developer guide.</p>
    /// <p>Jobs that are created without specifying a Glue version default to Glue 5.1.</p>
    pub fn glue_version(&self) -> ::std::option::Option<&str> {
        self.glue_version.as_deref()
    }
    /// <p>The number of workers of a defined <code>workerType</code> that are allocated when a job runs.</p>
    pub fn number_of_workers(&self) -> ::std::option::Option<i32> {
        self.number_of_workers
    }
    /// <p>The type of predefined worker that is allocated when a job runs. Accepts a value of G.025X, G.1X, G.2X, G.4X, G.8X, G.12X, G.16X, R.1X, R.2X, R.4X, or R.8X for Spark jobs. Accepts the value Z.2X for Ray jobs.</p>
    /// <ul>
    /// <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, 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.</p></li>
    /// <li>
    /// <p>For the <code>G.1X</code> 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.</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 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.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.12X</code> worker type, each worker maps to 12 DPU (48 vCPUs, 192 GB of memory) with 768GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.16X</code> worker type, each worker maps to 16 DPU (64 vCPUs, 256 GB of memory) with 1024GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.1X</code> worker type, each worker maps to 1 M-DPU (4 vCPUs, 32 GB of memory) with 94GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.2X</code> worker type, each worker maps to 2 M-DPU (8 vCPUs, 64 GB of memory) with 138GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.4X</code> worker type, each worker maps to 4 M-DPU (16 vCPUs, 128 GB of memory) with 256GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.8X</code> worker type, each worker maps to 8 M-DPU (32 vCPUs, 256 GB of memory) with 512GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, and provides up to 8 Ray workers based on the autoscaler.</p></li>
    /// </ul>
    pub fn worker_type(&self) -> ::std::option::Option<&crate::types::WorkerType> {
        self.worker_type.as_ref()
    }
    /// <p>The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.</p>
    pub fn code_gen_configuration_nodes(
        &self,
    ) -> ::std::option::Option<&::std::collections::HashMap<::std::string::String, crate::types::CodeGenConfigurationNode>> {
        self.code_gen_configuration_nodes.as_ref()
    }
    /// <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(&self) -> ::std::option::Option<&crate::types::ExecutionClass> {
        self.execution_class.as_ref()
    }
    /// <p>The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.</p>
    pub fn source_control_details(&self) -> ::std::option::Option<&crate::types::SourceControlDetails> {
        self.source_control_details.as_ref()
    }
    /// <p>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.</p>
    /// <p>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.</p>
    pub fn maintenance_window(&self) -> ::std::option::Option<&str> {
        self.maintenance_window.as_deref()
    }
}
impl ::std::fmt::Debug for CreateJobInput {
    fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
        let mut formatter = f.debug_struct("CreateJobInput");
        formatter.field("name", &self.name);
        formatter.field("job_mode", &self.job_mode);
        formatter.field("job_run_queuing_enabled", &self.job_run_queuing_enabled);
        formatter.field("description", &self.description);
        formatter.field("log_uri", &self.log_uri);
        formatter.field("role", &self.role);
        formatter.field("execution_property", &self.execution_property);
        formatter.field("command", &self.command);
        formatter.field("default_arguments", &self.default_arguments);
        formatter.field("non_overridable_arguments", &self.non_overridable_arguments);
        formatter.field("connections", &self.connections);
        formatter.field("max_retries", &self.max_retries);
        formatter.field("allocated_capacity", &self.allocated_capacity);
        formatter.field("timeout", &self.timeout);
        formatter.field("max_capacity", &self.max_capacity);
        formatter.field("security_configuration", &self.security_configuration);
        formatter.field("tags", &self.tags);
        formatter.field("notification_property", &self.notification_property);
        formatter.field("glue_version", &self.glue_version);
        formatter.field("number_of_workers", &self.number_of_workers);
        formatter.field("worker_type", &self.worker_type);
        formatter.field("code_gen_configuration_nodes", &"*** Sensitive Data Redacted ***");
        formatter.field("execution_class", &self.execution_class);
        formatter.field("source_control_details", &self.source_control_details);
        formatter.field("maintenance_window", &self.maintenance_window);
        formatter.finish()
    }
}
impl CreateJobInput {
    /// Creates a new builder-style object to manufacture [`CreateJobInput`](crate::operation::create_job::CreateJobInput).
    pub fn builder() -> crate::operation::create_job::builders::CreateJobInputBuilder {
        crate::operation::create_job::builders::CreateJobInputBuilder::default()
    }
}

/// A builder for [`CreateJobInput`](crate::operation::create_job::CreateJobInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default)]
#[non_exhaustive]
pub struct CreateJobInputBuilder {
    pub(crate) name: ::std::option::Option<::std::string::String>,
    pub(crate) job_mode: ::std::option::Option<crate::types::JobMode>,
    pub(crate) job_run_queuing_enabled: ::std::option::Option<bool>,
    pub(crate) description: ::std::option::Option<::std::string::String>,
    pub(crate) log_uri: ::std::option::Option<::std::string::String>,
    pub(crate) role: ::std::option::Option<::std::string::String>,
    pub(crate) execution_property: ::std::option::Option<crate::types::ExecutionProperty>,
    pub(crate) command: ::std::option::Option<crate::types::JobCommand>,
    pub(crate) default_arguments: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    pub(crate) non_overridable_arguments: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    pub(crate) connections: ::std::option::Option<crate::types::ConnectionsList>,
    pub(crate) max_retries: ::std::option::Option<i32>,
    pub(crate) allocated_capacity: ::std::option::Option<i32>,
    pub(crate) timeout: ::std::option::Option<i32>,
    pub(crate) max_capacity: ::std::option::Option<f64>,
    pub(crate) security_configuration: ::std::option::Option<::std::string::String>,
    pub(crate) tags: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    pub(crate) notification_property: ::std::option::Option<crate::types::NotificationProperty>,
    pub(crate) glue_version: ::std::option::Option<::std::string::String>,
    pub(crate) number_of_workers: ::std::option::Option<i32>,
    pub(crate) worker_type: ::std::option::Option<crate::types::WorkerType>,
    pub(crate) code_gen_configuration_nodes:
        ::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::CodeGenConfigurationNode>>,
    pub(crate) execution_class: ::std::option::Option<crate::types::ExecutionClass>,
    pub(crate) source_control_details: ::std::option::Option<crate::types::SourceControlDetails>,
    pub(crate) maintenance_window: ::std::option::Option<::std::string::String>,
}
impl CreateJobInputBuilder {
    /// <p>The name you assign to this job definition. It must be unique in your account.</p>
    /// This field is required.
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.name = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name you assign to this job definition. It must be unique in your account.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.name = input;
        self
    }
    /// <p>The name you assign to this job definition. It must be unique in your account.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        &self.name
    }
    /// <p>A mode that describes how a job was created. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>SCRIPT</code> - The job was created using the Glue Studio script editor.</p></li>
    /// <li>
    /// <p><code>VISUAL</code> - The job was created using the Glue Studio visual editor.</p></li>
    /// <li>
    /// <p><code>NOTEBOOK</code> - The job was created using an interactive sessions notebook.</p></li>
    /// </ul>
    /// <p>When the <code>JobMode</code> field is missing or null, <code>SCRIPT</code> is assigned as the default value.</p>
    pub fn job_mode(mut self, input: crate::types::JobMode) -> Self {
        self.job_mode = ::std::option::Option::Some(input);
        self
    }
    /// <p>A mode that describes how a job was created. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>SCRIPT</code> - The job was created using the Glue Studio script editor.</p></li>
    /// <li>
    /// <p><code>VISUAL</code> - The job was created using the Glue Studio visual editor.</p></li>
    /// <li>
    /// <p><code>NOTEBOOK</code> - The job was created using an interactive sessions notebook.</p></li>
    /// </ul>
    /// <p>When the <code>JobMode</code> field is missing or null, <code>SCRIPT</code> is assigned as the default value.</p>
    pub fn set_job_mode(mut self, input: ::std::option::Option<crate::types::JobMode>) -> Self {
        self.job_mode = input;
        self
    }
    /// <p>A mode that describes how a job was created. Valid values are:</p>
    /// <ul>
    /// <li>
    /// <p><code>SCRIPT</code> - The job was created using the Glue Studio script editor.</p></li>
    /// <li>
    /// <p><code>VISUAL</code> - The job was created using the Glue Studio visual editor.</p></li>
    /// <li>
    /// <p><code>NOTEBOOK</code> - The job was created using an interactive sessions notebook.</p></li>
    /// </ul>
    /// <p>When the <code>JobMode</code> field is missing or null, <code>SCRIPT</code> is assigned as the default value.</p>
    pub fn get_job_mode(&self) -> &::std::option::Option<crate::types::JobMode> {
        &self.job_mode
    }
    /// <p>Specifies whether job run queuing is enabled for the job runs for this job.</p>
    /// <p>A value of true means job run queuing is enabled for the job runs. If false or not populated, the job runs will not be considered for queueing.</p>
    /// <p>If this field does not match the value set in the job run, then the value from the job run field will be used.</p>
    pub fn job_run_queuing_enabled(mut self, input: bool) -> Self {
        self.job_run_queuing_enabled = ::std::option::Option::Some(input);
        self
    }
    /// <p>Specifies whether job run queuing is enabled for the job runs for this job.</p>
    /// <p>A value of true means job run queuing is enabled for the job runs. If false or not populated, the job runs will not be considered for queueing.</p>
    /// <p>If this field does not match the value set in the job run, then the value from the job run field will be used.</p>
    pub fn set_job_run_queuing_enabled(mut self, input: ::std::option::Option<bool>) -> Self {
        self.job_run_queuing_enabled = input;
        self
    }
    /// <p>Specifies whether job run queuing is enabled for the job runs for this job.</p>
    /// <p>A value of true means job run queuing is enabled for the job runs. If false or not populated, the job runs will not be considered for queueing.</p>
    /// <p>If this field does not match the value set in the job run, then the value from the job run field will be used.</p>
    pub fn get_job_run_queuing_enabled(&self) -> &::std::option::Option<bool> {
        &self.job_run_queuing_enabled
    }
    /// <p>Description of the job being defined.</p>
    pub fn description(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.description = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>Description of the job being defined.</p>
    pub fn set_description(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.description = input;
        self
    }
    /// <p>Description of the job being defined.</p>
    pub fn get_description(&self) -> &::std::option::Option<::std::string::String> {
        &self.description
    }
    /// <p>This field is reserved for future use.</p>
    pub fn log_uri(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.log_uri = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>This field is reserved for future use.</p>
    pub fn set_log_uri(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.log_uri = input;
        self
    }
    /// <p>This field is reserved for future use.</p>
    pub fn get_log_uri(&self) -> &::std::option::Option<::std::string::String> {
        &self.log_uri
    }
    /// <p>The name or Amazon Resource Name (ARN) of the IAM role associated with this job.</p>
    /// This field is required.
    pub fn role(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.role = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name or Amazon Resource Name (ARN) of the IAM role associated with this job.</p>
    pub fn set_role(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.role = input;
        self
    }
    /// <p>The name or Amazon Resource Name (ARN) of the IAM role associated with this job.</p>
    pub fn get_role(&self) -> &::std::option::Option<::std::string::String> {
        &self.role
    }
    /// <p>An <code>ExecutionProperty</code> specifying the maximum number of concurrent runs allowed for this job.</p>
    pub fn execution_property(mut self, input: crate::types::ExecutionProperty) -> Self {
        self.execution_property = ::std::option::Option::Some(input);
        self
    }
    /// <p>An <code>ExecutionProperty</code> specifying the maximum number of concurrent runs allowed for this job.</p>
    pub fn set_execution_property(mut self, input: ::std::option::Option<crate::types::ExecutionProperty>) -> Self {
        self.execution_property = input;
        self
    }
    /// <p>An <code>ExecutionProperty</code> specifying the maximum number of concurrent runs allowed for this job.</p>
    pub fn get_execution_property(&self) -> &::std::option::Option<crate::types::ExecutionProperty> {
        &self.execution_property
    }
    /// <p>The <code>JobCommand</code> that runs this job.</p>
    /// This field is required.
    pub fn command(mut self, input: crate::types::JobCommand) -> Self {
        self.command = ::std::option::Option::Some(input);
        self
    }
    /// <p>The <code>JobCommand</code> that runs this job.</p>
    pub fn set_command(mut self, input: ::std::option::Option<crate::types::JobCommand>) -> Self {
        self.command = input;
        self
    }
    /// <p>The <code>JobCommand</code> that runs this job.</p>
    pub fn get_command(&self) -> &::std::option::Option<crate::types::JobCommand> {
        &self.command
    }
    /// Adds a key-value pair to `default_arguments`.
    ///
    /// To override the contents of this collection use [`set_default_arguments`](Self::set_default_arguments).
    ///
    /// <p>The default arguments for every run of this job, specified as name-value pairs.</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 default_arguments(
        mut self,
        k: impl ::std::convert::Into<::std::string::String>,
        v: impl ::std::convert::Into<::std::string::String>,
    ) -> Self {
        let mut hash_map = self.default_arguments.unwrap_or_default();
        hash_map.insert(k.into(), v.into());
        self.default_arguments = ::std::option::Option::Some(hash_map);
        self
    }
    /// <p>The default arguments for every run of this job, specified as name-value pairs.</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_default_arguments(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    ) -> Self {
        self.default_arguments = input;
        self
    }
    /// <p>The default arguments for every run of this job, specified as name-value pairs.</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_default_arguments(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        &self.default_arguments
    }
    /// Adds a key-value pair to `non_overridable_arguments`.
    ///
    /// To override the contents of this collection use [`set_non_overridable_arguments`](Self::set_non_overridable_arguments).
    ///
    /// <p>Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.</p>
    pub fn non_overridable_arguments(
        mut self,
        k: impl ::std::convert::Into<::std::string::String>,
        v: impl ::std::convert::Into<::std::string::String>,
    ) -> Self {
        let mut hash_map = self.non_overridable_arguments.unwrap_or_default();
        hash_map.insert(k.into(), v.into());
        self.non_overridable_arguments = ::std::option::Option::Some(hash_map);
        self
    }
    /// <p>Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.</p>
    pub fn set_non_overridable_arguments(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
    ) -> Self {
        self.non_overridable_arguments = input;
        self
    }
    /// <p>Arguments for this job that are not overridden when providing job arguments in a job run, specified as name-value pairs.</p>
    pub fn get_non_overridable_arguments(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        &self.non_overridable_arguments
    }
    /// <p>The connections used for this job.</p>
    pub fn connections(mut self, input: crate::types::ConnectionsList) -> Self {
        self.connections = ::std::option::Option::Some(input);
        self
    }
    /// <p>The connections used for this job.</p>
    pub fn set_connections(mut self, input: ::std::option::Option<crate::types::ConnectionsList>) -> Self {
        self.connections = input;
        self
    }
    /// <p>The connections used for this job.</p>
    pub fn get_connections(&self) -> &::std::option::Option<crate::types::ConnectionsList> {
        &self.connections
    }
    /// <p>The maximum number of times to retry this job if it fails.</p>
    pub fn max_retries(mut self, input: i32) -> Self {
        self.max_retries = ::std::option::Option::Some(input);
        self
    }
    /// <p>The maximum number of times to retry this job if it fails.</p>
    pub fn set_max_retries(mut self, input: ::std::option::Option<i32>) -> Self {
        self.max_retries = input;
        self
    }
    /// <p>The maximum number of times to retry this job if it fails.</p>
    pub fn get_max_retries(&self) -> &::std::option::Option<i32> {
        &self.max_retries
    }
    /// <p>This parameter is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <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.allocated_capacity = ::std::option::Option::Some(input);
        self
    }
    /// <p>This parameter is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <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.allocated_capacity = input;
        self
    }
    /// <p>This parameter is deprecated. Use <code>MaxCapacity</code> instead.</p>
    /// <p>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 <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.allocated_capacity
    }
    /// <p>The job 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.</p>
    /// <p>Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.</p>
    /// <p>When the value is left blank, the timeout is defaulted to 2,880 minutes for Glue version 4.0 and earlier, or 480 minutes for Glue version 5.0 and later.</p>
    /// <p>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.</p>
    /// <p>For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.</p>
    pub fn timeout(mut self, input: i32) -> Self {
        self.timeout = ::std::option::Option::Some(input);
        self
    }
    /// <p>The job 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.</p>
    /// <p>Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.</p>
    /// <p>When the value is left blank, the timeout is defaulted to 2,880 minutes for Glue version 4.0 and earlier, or 480 minutes for Glue version 5.0 and later.</p>
    /// <p>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.</p>
    /// <p>For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.</p>
    pub fn set_timeout(mut self, input: ::std::option::Option<i32>) -> Self {
        self.timeout = input;
        self
    }
    /// <p>The job 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.</p>
    /// <p>Jobs must have timeout values less than 7 days or 10080 minutes. Otherwise, the jobs will throw an exception.</p>
    /// <p>When the value is left blank, the timeout is defaulted to 2,880 minutes for Glue version 4.0 and earlier, or 480 minutes for Glue version 5.0 and later.</p>
    /// <p>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.</p>
    /// <p>For streaming jobs, if you have set up a maintenance window, it will be restarted during the maintenance window after 7 days.</p>
    pub fn get_timeout(&self) -> &::std::option::Option<i32> {
        &self.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.max_capacity = ::std::option::Option::Some(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.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.max_capacity
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job.</p>
    pub fn security_configuration(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.security_configuration = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job.</p>
    pub fn set_security_configuration(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.security_configuration = input;
        self
    }
    /// <p>The name of the <code>SecurityConfiguration</code> structure to be used with this job.</p>
    pub fn get_security_configuration(&self) -> &::std::option::Option<::std::string::String> {
        &self.security_configuration
    }
    /// Adds a key-value pair to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>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 <a href="https://docs.aws.amazon.com/glue/latest/dg/monitor-tags.html">Amazon Web Services Tags in Glue</a> in the developer guide.</p>
    pub fn tags(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        let mut hash_map = self.tags.unwrap_or_default();
        hash_map.insert(k.into(), v.into());
        self.tags = ::std::option::Option::Some(hash_map);
        self
    }
    /// <p>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 <a href="https://docs.aws.amazon.com/glue/latest/dg/monitor-tags.html">Amazon Web Services Tags in Glue</a> in the developer guide.</p>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.tags = input;
        self
    }
    /// <p>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 <a href="https://docs.aws.amazon.com/glue/latest/dg/monitor-tags.html">Amazon Web Services Tags in Glue</a> in the developer guide.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        &self.tags
    }
    /// <p>Specifies configuration properties of a job notification.</p>
    pub fn notification_property(mut self, input: crate::types::NotificationProperty) -> Self {
        self.notification_property = ::std::option::Option::Some(input);
        self
    }
    /// <p>Specifies configuration properties of a job notification.</p>
    pub fn set_notification_property(mut self, input: ::std::option::Option<crate::types::NotificationProperty>) -> Self {
        self.notification_property = input;
        self
    }
    /// <p>Specifies configuration properties of a job notification.</p>
    pub fn get_notification_property(&self) -> &::std::option::Option<crate::types::NotificationProperty> {
        &self.notification_property
    }
    /// <p>In Spark jobs, <code>GlueVersion</code> 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.</p>
    /// <p>Ray jobs should set <code>GlueVersion</code> to <code>4.0</code> or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the <code>Runtime</code> parameter of the Job command.</p>
    /// <p>For more information about the available Glue versions and corresponding Spark and Python versions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Glue version</a> in the developer guide.</p>
    /// <p>Jobs that are created without specifying a Glue version default to Glue 5.1.</p>
    pub fn glue_version(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.glue_version = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>In Spark jobs, <code>GlueVersion</code> 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.</p>
    /// <p>Ray jobs should set <code>GlueVersion</code> to <code>4.0</code> or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the <code>Runtime</code> parameter of the Job command.</p>
    /// <p>For more information about the available Glue versions and corresponding Spark and Python versions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Glue version</a> in the developer guide.</p>
    /// <p>Jobs that are created without specifying a Glue version default to Glue 5.1.</p>
    pub fn set_glue_version(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.glue_version = input;
        self
    }
    /// <p>In Spark jobs, <code>GlueVersion</code> 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.</p>
    /// <p>Ray jobs should set <code>GlueVersion</code> to <code>4.0</code> or greater. However, the versions of Ray, Python and additional libraries available in your Ray job are determined by the <code>Runtime</code> parameter of the Job command.</p>
    /// <p>For more information about the available Glue versions and corresponding Spark and Python versions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Glue version</a> in the developer guide.</p>
    /// <p>Jobs that are created without specifying a Glue version default to Glue 5.1.</p>
    pub fn get_glue_version(&self) -> &::std::option::Option<::std::string::String> {
        &self.glue_version
    }
    /// <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.number_of_workers = ::std::option::Option::Some(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.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.number_of_workers
    }
    /// <p>The type of predefined worker that is allocated when a job runs. Accepts a value of G.025X, G.1X, G.2X, G.4X, G.8X, G.12X, G.16X, R.1X, R.2X, R.4X, or R.8X for Spark jobs. Accepts the value Z.2X for Ray jobs.</p>
    /// <ul>
    /// <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, 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.</p></li>
    /// <li>
    /// <p>For the <code>G.1X</code> 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.</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 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.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.12X</code> worker type, each worker maps to 12 DPU (48 vCPUs, 192 GB of memory) with 768GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.16X</code> worker type, each worker maps to 16 DPU (64 vCPUs, 256 GB of memory) with 1024GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.1X</code> worker type, each worker maps to 1 M-DPU (4 vCPUs, 32 GB of memory) with 94GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.2X</code> worker type, each worker maps to 2 M-DPU (8 vCPUs, 64 GB of memory) with 138GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.4X</code> worker type, each worker maps to 4 M-DPU (16 vCPUs, 128 GB of memory) with 256GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.8X</code> worker type, each worker maps to 8 M-DPU (32 vCPUs, 256 GB of memory) with 512GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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.worker_type = ::std::option::Option::Some(input);
        self
    }
    /// <p>The type of predefined worker that is allocated when a job runs. Accepts a value of G.025X, G.1X, G.2X, G.4X, G.8X, G.12X, G.16X, R.1X, R.2X, R.4X, or R.8X for Spark jobs. Accepts the value Z.2X for Ray jobs.</p>
    /// <ul>
    /// <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, 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.</p></li>
    /// <li>
    /// <p>For the <code>G.1X</code> 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.</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 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.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.12X</code> worker type, each worker maps to 12 DPU (48 vCPUs, 192 GB of memory) with 768GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.16X</code> worker type, each worker maps to 16 DPU (64 vCPUs, 256 GB of memory) with 1024GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.1X</code> worker type, each worker maps to 1 M-DPU (4 vCPUs, 32 GB of memory) with 94GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.2X</code> worker type, each worker maps to 2 M-DPU (8 vCPUs, 64 GB of memory) with 138GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.4X</code> worker type, each worker maps to 4 M-DPU (16 vCPUs, 128 GB of memory) with 256GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.8X</code> worker type, each worker maps to 8 M-DPU (32 vCPUs, 256 GB of memory) with 512GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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.worker_type = input;
        self
    }
    /// <p>The type of predefined worker that is allocated when a job runs. Accepts a value of G.025X, G.1X, G.2X, G.4X, G.8X, G.12X, G.16X, R.1X, R.2X, R.4X, or R.8X for Spark jobs. Accepts the value Z.2X for Ray jobs.</p>
    /// <ul>
    /// <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, 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.</p></li>
    /// <li>
    /// <p>For the <code>G.1X</code> 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.</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 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.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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 select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.12X</code> worker type, each worker maps to 12 DPU (48 vCPUs, 192 GB of memory) with 768GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>G.16X</code> worker type, each worker maps to 16 DPU (64 vCPUs, 256 GB of memory) with 1024GB disk, and provides 1 executor per worker. We recommend this worker type for jobs with very large workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.1X</code> worker type, each worker maps to 1 M-DPU (4 vCPUs, 32 GB of memory) with 94GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.2X</code> worker type, each worker maps to 2 M-DPU (8 vCPUs, 64 GB of memory) with 138GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.4X</code> worker type, each worker maps to 4 M-DPU (16 vCPUs, 128 GB of memory) with 256GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</p></li>
    /// <li>
    /// <p>For the <code>R.8X</code> worker type, each worker maps to 8 M-DPU (32 vCPUs, 256 GB of memory) with 512GB disk, and provides 1 executor per worker. We recommend this worker type for memory-intensive workloads. This worker type is available only for Glue version 4.0 or later Spark ETL jobs in select Amazon Web Services Regions. For the supported Regions, see <a href="https://docs.aws.amazon.com/glue/latest/dg/add-job.html">Configuring job properties for Spark jobs</a> in the developer guide.</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, 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.worker_type
    }
    /// Adds a key-value pair to `code_gen_configuration_nodes`.
    ///
    /// To override the contents of this collection use [`set_code_gen_configuration_nodes`](Self::set_code_gen_configuration_nodes).
    ///
    /// <p>The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.</p>
    pub fn code_gen_configuration_nodes(
        mut self,
        k: impl ::std::convert::Into<::std::string::String>,
        v: crate::types::CodeGenConfigurationNode,
    ) -> Self {
        let mut hash_map = self.code_gen_configuration_nodes.unwrap_or_default();
        hash_map.insert(k.into(), v);
        self.code_gen_configuration_nodes = ::std::option::Option::Some(hash_map);
        self
    }
    /// <p>The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.</p>
    pub fn set_code_gen_configuration_nodes(
        mut self,
        input: ::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::CodeGenConfigurationNode>>,
    ) -> Self {
        self.code_gen_configuration_nodes = input;
        self
    }
    /// <p>The representation of a directed acyclic graph on which both the Glue Studio visual component and Glue Studio code generation is based.</p>
    pub fn get_code_gen_configuration_nodes(
        &self,
    ) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::CodeGenConfigurationNode>> {
        &self.code_gen_configuration_nodes
    }
    /// <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.execution_class = ::std::option::Option::Some(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.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.execution_class
    }
    /// <p>The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.</p>
    pub fn source_control_details(mut self, input: crate::types::SourceControlDetails) -> Self {
        self.source_control_details = ::std::option::Option::Some(input);
        self
    }
    /// <p>The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.</p>
    pub fn set_source_control_details(mut self, input: ::std::option::Option<crate::types::SourceControlDetails>) -> Self {
        self.source_control_details = input;
        self
    }
    /// <p>The details for a source control configuration for a job, allowing synchronization of job artifacts to or from a remote repository.</p>
    pub fn get_source_control_details(&self) -> &::std::option::Option<crate::types::SourceControlDetails> {
        &self.source_control_details
    }
    /// <p>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.</p>
    /// <p>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.</p>
    pub fn maintenance_window(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.maintenance_window = ::std::option::Option::Some(input.into());
        self
    }
    /// <p>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.</p>
    /// <p>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.</p>
    pub fn set_maintenance_window(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.maintenance_window = input;
        self
    }
    /// <p>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.</p>
    /// <p>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.</p>
    pub fn get_maintenance_window(&self) -> &::std::option::Option<::std::string::String> {
        &self.maintenance_window
    }
    /// Consumes the builder and constructs a [`CreateJobInput`](crate::operation::create_job::CreateJobInput).
    pub fn build(self) -> ::std::result::Result<crate::operation::create_job::CreateJobInput, ::aws_smithy_types::error::operation::BuildError> {
        ::std::result::Result::Ok(crate::operation::create_job::CreateJobInput {
            name: self.name,
            job_mode: self.job_mode,
            job_run_queuing_enabled: self.job_run_queuing_enabled,
            description: self.description,
            log_uri: self.log_uri,
            role: self.role,
            execution_property: self.execution_property,
            command: self.command,
            default_arguments: self.default_arguments,
            non_overridable_arguments: self.non_overridable_arguments,
            connections: self.connections,
            max_retries: self.max_retries,
            allocated_capacity: self.allocated_capacity,
            timeout: self.timeout,
            max_capacity: self.max_capacity,
            security_configuration: self.security_configuration,
            tags: self.tags,
            notification_property: self.notification_property,
            glue_version: self.glue_version,
            number_of_workers: self.number_of_workers,
            worker_type: self.worker_type,
            code_gen_configuration_nodes: self.code_gen_configuration_nodes,
            execution_class: self.execution_class,
            source_control_details: self.source_control_details,
            maintenance_window: self.maintenance_window,
        })
    }
}
impl ::std::fmt::Debug for CreateJobInputBuilder {
    fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
        let mut formatter = f.debug_struct("CreateJobInputBuilder");
        formatter.field("name", &self.name);
        formatter.field("job_mode", &self.job_mode);
        formatter.field("job_run_queuing_enabled", &self.job_run_queuing_enabled);
        formatter.field("description", &self.description);
        formatter.field("log_uri", &self.log_uri);
        formatter.field("role", &self.role);
        formatter.field("execution_property", &self.execution_property);
        formatter.field("command", &self.command);
        formatter.field("default_arguments", &self.default_arguments);
        formatter.field("non_overridable_arguments", &self.non_overridable_arguments);
        formatter.field("connections", &self.connections);
        formatter.field("max_retries", &self.max_retries);
        formatter.field("allocated_capacity", &self.allocated_capacity);
        formatter.field("timeout", &self.timeout);
        formatter.field("max_capacity", &self.max_capacity);
        formatter.field("security_configuration", &self.security_configuration);
        formatter.field("tags", &self.tags);
        formatter.field("notification_property", &self.notification_property);
        formatter.field("glue_version", &self.glue_version);
        formatter.field("number_of_workers", &self.number_of_workers);
        formatter.field("worker_type", &self.worker_type);
        formatter.field("code_gen_configuration_nodes", &"*** Sensitive Data Redacted ***");
        formatter.field("execution_class", &self.execution_class);
        formatter.field("source_control_details", &self.source_control_details);
        formatter.field("maintenance_window", &self.maintenance_window);
        formatter.finish()
    }
}