// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_data_source_from_redshift::_create_data_source_from_redshift_output::CreateDataSourceFromRedshiftOutputBuilder;

pub use crate::operation::create_data_source_from_redshift::_create_data_source_from_redshift_input::CreateDataSourceFromRedshiftInputBuilder;

impl CreateDataSourceFromRedshiftInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_data_source_from_redshift();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateDataSourceFromRedshift`.
///
/// <p>Creates a <code>DataSource</code> from a database hosted on an Amazon Redshift cluster. A <code>DataSource</code> references data that can be used to perform either <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.</p>
/// <p> <code>CreateDataSourceFromRedshift</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRedshift</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in <code>COMPLETED</code> or <code>PENDING</code> states can be used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. </p>
/// <p> If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response. </p>
/// <p>The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a <code>SelectSqlQuery</code> query. Amazon ML executes an <code>Unload</code> command in Amazon Redshift to transfer the result set of the <code>SelectSqlQuery</code> query to <code>S3StagingLocation</code>.</p>
/// <p>After the <code>DataSource</code> has been created, it's ready for use in evaluations and batch predictions. If you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also requires a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.</p>
/// <p>You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call <code>GetDataSource</code> for an existing datasource and copy the values to a <code>CreateDataSource</code> call. Change the settings that you want to change and make sure that all required fields have the appropriate values.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateDataSourceFromRedshiftFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_data_source_from_redshift::builders::CreateDataSourceFromRedshiftInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftOutput,
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftError,
    > for CreateDataSourceFromRedshiftFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftOutput,
            crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateDataSourceFromRedshiftFluentBuilder {
    /// Creates a new `CreateDataSourceFromRedshift`.
    pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
        Self {
            handle,
            inner: ::std::default::Default::default(),
            config_override: ::std::option::Option::None,
        }
    }
    /// Access the CreateDataSourceFromRedshift as a reference.
    pub fn as_input(&self) -> &crate::operation::create_data_source_from_redshift::builders::CreateDataSourceFromRedshiftInputBuilder {
        &self.inner
    }
    /// Sends the request and returns the response.
    ///
    /// If an error occurs, an `SdkError` will be returned with additional details that
    /// can be matched against.
    ///
    /// By default, any retryable failures will be retried twice. Retry behavior
    /// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
    /// set when configuring the client.
    pub async fn send(
        self,
    ) -> ::std::result::Result<
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let input = self
            .inner
            .build()
            .map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
        let runtime_plugins = crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshift::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshift::orchestrate(&runtime_plugins, input).await
    }

    /// Consumes this builder, creating a customizable operation that can be modified before being sent.
    pub fn customize(
        self,
    ) -> crate::client::customize::CustomizableOperation<
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftOutput,
        crate::operation::create_data_source_from_redshift::CreateDataSourceFromRedshiftError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
        self.set_config_override(Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</p>
    pub fn data_source_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.data_source_id(input.into());
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</p>
    pub fn set_data_source_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_data_source_id(input);
        self
    }
    /// <p>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</p>
    pub fn get_data_source_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_data_source_id()
    }
    /// <p>A user-supplied name or description of the <code>DataSource</code>. </p>
    pub fn data_source_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.data_source_name(input.into());
        self
    }
    /// <p>A user-supplied name or description of the <code>DataSource</code>. </p>
    pub fn set_data_source_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_data_source_name(input);
        self
    }
    /// <p>A user-supplied name or description of the <code>DataSource</code>. </p>
    pub fn get_data_source_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_data_source_name()
    }
    /// <p>The data specification of an Amazon Redshift <code>DataSource</code>:</p>
    /// <ul>
    /// <li> <p>DatabaseInformation -</p>
    /// <ul>
    /// <li> <p> <code>DatabaseName</code> - The name of the Amazon Redshift database.</p> </li>
    /// <li> <p> <code> ClusterIdentifier</code> - The unique ID for the Amazon Redshift cluster.</p> </li>
    /// </ul> </li>
    /// <li> <p>DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.</p> </li>
    /// <li> <p>SelectSqlQuery - The query that is used to retrieve the observation data for the <code>Datasource</code>.</p> </li>
    /// <li> <p>S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the <code>SelectSqlQuery</code> query is stored in this location.</p> </li>
    /// <li> <p>DataSchemaUri - The Amazon S3 location of the <code>DataSchema</code>.</p> </li>
    /// <li> <p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p> </li>
    /// <li> <p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>DataSource</code>.</p> <p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code> </p> </li>
    /// </ul>
    pub fn data_spec(mut self, input: crate::types::RedshiftDataSpec) -> Self {
        self.inner = self.inner.data_spec(input);
        self
    }
    /// <p>The data specification of an Amazon Redshift <code>DataSource</code>:</p>
    /// <ul>
    /// <li> <p>DatabaseInformation -</p>
    /// <ul>
    /// <li> <p> <code>DatabaseName</code> - The name of the Amazon Redshift database.</p> </li>
    /// <li> <p> <code> ClusterIdentifier</code> - The unique ID for the Amazon Redshift cluster.</p> </li>
    /// </ul> </li>
    /// <li> <p>DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.</p> </li>
    /// <li> <p>SelectSqlQuery - The query that is used to retrieve the observation data for the <code>Datasource</code>.</p> </li>
    /// <li> <p>S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the <code>SelectSqlQuery</code> query is stored in this location.</p> </li>
    /// <li> <p>DataSchemaUri - The Amazon S3 location of the <code>DataSchema</code>.</p> </li>
    /// <li> <p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p> </li>
    /// <li> <p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>DataSource</code>.</p> <p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code> </p> </li>
    /// </ul>
    pub fn set_data_spec(mut self, input: ::std::option::Option<crate::types::RedshiftDataSpec>) -> Self {
        self.inner = self.inner.set_data_spec(input);
        self
    }
    /// <p>The data specification of an Amazon Redshift <code>DataSource</code>:</p>
    /// <ul>
    /// <li> <p>DatabaseInformation -</p>
    /// <ul>
    /// <li> <p> <code>DatabaseName</code> - The name of the Amazon Redshift database.</p> </li>
    /// <li> <p> <code> ClusterIdentifier</code> - The unique ID for the Amazon Redshift cluster.</p> </li>
    /// </ul> </li>
    /// <li> <p>DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.</p> </li>
    /// <li> <p>SelectSqlQuery - The query that is used to retrieve the observation data for the <code>Datasource</code>.</p> </li>
    /// <li> <p>S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the <code>SelectSqlQuery</code> query is stored in this location.</p> </li>
    /// <li> <p>DataSchemaUri - The Amazon S3 location of the <code>DataSchema</code>.</p> </li>
    /// <li> <p>DataSchema - A JSON string representing the schema. This is not required if <code>DataSchemaUri</code> is specified. </p> </li>
    /// <li> <p>DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the <code>DataSource</code>.</p> <p> Sample - <code> "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"</code> </p> </li>
    /// </ul>
    pub fn get_data_spec(&self) -> &::std::option::Option<crate::types::RedshiftDataSpec> {
        self.inner.get_data_spec()
    }
    /// <p>A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:</p>
    /// <ul>
    /// <li> <p>A security group to allow Amazon ML to execute the <code>SelectSqlQuery</code> query on an Amazon Redshift cluster</p> </li>
    /// <li> <p>An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the <code>S3StagingLocation</code> </p> </li>
    /// </ul>
    pub fn role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.role_arn(input.into());
        self
    }
    /// <p>A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:</p>
    /// <ul>
    /// <li> <p>A security group to allow Amazon ML to execute the <code>SelectSqlQuery</code> query on an Amazon Redshift cluster</p> </li>
    /// <li> <p>An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the <code>S3StagingLocation</code> </p> </li>
    /// </ul>
    pub fn set_role_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_role_arn(input);
        self
    }
    /// <p>A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:</p>
    /// <ul>
    /// <li> <p>A security group to allow Amazon ML to execute the <code>SelectSqlQuery</code> query on an Amazon Redshift cluster</p> </li>
    /// <li> <p>An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the <code>S3StagingLocation</code> </p> </li>
    /// </ul>
    pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_role_arn()
    }
    /// <p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code>DataSource</code> needs to be used for <code>MLModel</code> training.</p>
    pub fn compute_statistics(mut self, input: bool) -> Self {
        self.inner = self.inner.compute_statistics(input);
        self
    }
    /// <p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code>DataSource</code> needs to be used for <code>MLModel</code> training.</p>
    pub fn set_compute_statistics(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_compute_statistics(input);
        self
    }
    /// <p>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code>DataSource</code> needs to be used for <code>MLModel</code> training.</p>
    pub fn get_compute_statistics(&self) -> &::std::option::Option<bool> {
        self.inner.get_compute_statistics()
    }
}