aws-sdk-supplychain 1.97.0

AWS SDK for AWS Supply Chain
Documentation
// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_data_lake_dataset::_create_data_lake_dataset_input::CreateDataLakeDatasetInputBuilder;

pub use crate::operation::create_data_lake_dataset::_create_data_lake_dataset_output::CreateDataLakeDatasetOutputBuilder;

impl crate::operation::create_data_lake_dataset::builders::CreateDataLakeDatasetInputBuilder {
    /// 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_lake_dataset::CreateDataLakeDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_data_lake_dataset::CreateDataLakeDatasetError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_data_lake_dataset();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateDataLakeDataset`.
///
/// <p>Enables you to programmatically create an Amazon Web Services Supply Chain data lake dataset. Developers can create the datasets using their pre-defined or custom schema for a given instance ID, namespace, and dataset name.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateDataLakeDatasetFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_data_lake_dataset::builders::CreateDataLakeDatasetInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_data_lake_dataset::CreateDataLakeDatasetOutput,
        crate::operation::create_data_lake_dataset::CreateDataLakeDatasetError,
    > for CreateDataLakeDatasetFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_data_lake_dataset::CreateDataLakeDatasetOutput,
            crate::operation::create_data_lake_dataset::CreateDataLakeDatasetError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateDataLakeDatasetFluentBuilder {
    /// Creates a new `CreateDataLakeDatasetFluentBuilder`.
    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 CreateDataLakeDataset as a reference.
    pub fn as_input(&self) -> &crate::operation::create_data_lake_dataset::builders::CreateDataLakeDatasetInputBuilder {
        &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_lake_dataset::CreateDataLakeDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_data_lake_dataset::CreateDataLakeDatasetError,
            ::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_lake_dataset::CreateDataLakeDataset::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_data_lake_dataset::CreateDataLakeDataset::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_lake_dataset::CreateDataLakeDatasetOutput,
        crate::operation::create_data_lake_dataset::CreateDataLakeDatasetError,
        Self,
    > {
        crate::client::customize::CustomizableOperation::new(self)
    }
    pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
        self.set_config_override(::std::option::Option::Some(config_override.into()));
        self
    }

    pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
        self.config_override = config_override;
        self
    }
    /// <p>The Amazon Web Services Supply Chain instance identifier.</p>
    pub fn instance_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.instance_id(input.into());
        self
    }
    /// <p>The Amazon Web Services Supply Chain instance identifier.</p>
    pub fn set_instance_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_instance_id(input);
        self
    }
    /// <p>The Amazon Web Services Supply Chain instance identifier.</p>
    pub fn get_instance_id(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_instance_id()
    }
    /// <p>The namespace of the dataset, besides the custom defined namespace, every instance comes with below pre-defined namespaces:</p>
    /// <ul>
    /// <li>
    /// <p><b>asc</b> - For information on the Amazon Web Services Supply Chain supported datasets see <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p></li>
    /// <li>
    /// <p><b>default</b> - For datasets with custom user-defined schemas.</p></li>
    /// </ul>
    pub fn namespace(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.namespace(input.into());
        self
    }
    /// <p>The namespace of the dataset, besides the custom defined namespace, every instance comes with below pre-defined namespaces:</p>
    /// <ul>
    /// <li>
    /// <p><b>asc</b> - For information on the Amazon Web Services Supply Chain supported datasets see <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p></li>
    /// <li>
    /// <p><b>default</b> - For datasets with custom user-defined schemas.</p></li>
    /// </ul>
    pub fn set_namespace(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_namespace(input);
        self
    }
    /// <p>The namespace of the dataset, besides the custom defined namespace, every instance comes with below pre-defined namespaces:</p>
    /// <ul>
    /// <li>
    /// <p><b>asc</b> - For information on the Amazon Web Services Supply Chain supported datasets see <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p></li>
    /// <li>
    /// <p><b>default</b> - For datasets with custom user-defined schemas.</p></li>
    /// </ul>
    pub fn get_namespace(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_namespace()
    }
    /// <p>The name of the dataset. For <b>asc</b> name space, the name must be one of the supported data entities under <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p>
    pub fn name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.name(input.into());
        self
    }
    /// <p>The name of the dataset. For <b>asc</b> name space, the name must be one of the supported data entities under <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p>
    pub fn set_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_name(input);
        self
    }
    /// <p>The name of the dataset. For <b>asc</b> name space, the name must be one of the supported data entities under <a href="https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html">https://docs.aws.amazon.com/aws-supply-chain/latest/userguide/data-model-asc.html</a>.</p>
    pub fn get_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_name()
    }
    /// <p>The custom schema of the data lake dataset and required for dataset in <b>default</b> and custom namespaces.</p>
    pub fn schema(mut self, input: crate::types::DataLakeDatasetSchema) -> Self {
        self.inner = self.inner.schema(input);
        self
    }
    /// <p>The custom schema of the data lake dataset and required for dataset in <b>default</b> and custom namespaces.</p>
    pub fn set_schema(mut self, input: ::std::option::Option<crate::types::DataLakeDatasetSchema>) -> Self {
        self.inner = self.inner.set_schema(input);
        self
    }
    /// <p>The custom schema of the data lake dataset and required for dataset in <b>default</b> and custom namespaces.</p>
    pub fn get_schema(&self) -> &::std::option::Option<crate::types::DataLakeDatasetSchema> {
        self.inner.get_schema()
    }
    /// <p>The description of the dataset.</p>
    pub fn description(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.description(input.into());
        self
    }
    /// <p>The description of the dataset.</p>
    pub fn set_description(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_description(input);
        self
    }
    /// <p>The description of the dataset.</p>
    pub fn get_description(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_description()
    }
    /// <p>The partition specification of the dataset. Partitioning can effectively improve the dataset query performance by reducing the amount of data scanned during query execution. But partitioning or not will affect how data get ingested by data ingestion methods, such as SendDataIntegrationEvent's dataset UPSERT will upsert records within partition (instead of within whole dataset). For more details, refer to those data ingestion documentations.</p>
    pub fn partition_spec(mut self, input: crate::types::DataLakeDatasetPartitionSpec) -> Self {
        self.inner = self.inner.partition_spec(input);
        self
    }
    /// <p>The partition specification of the dataset. Partitioning can effectively improve the dataset query performance by reducing the amount of data scanned during query execution. But partitioning or not will affect how data get ingested by data ingestion methods, such as SendDataIntegrationEvent's dataset UPSERT will upsert records within partition (instead of within whole dataset). For more details, refer to those data ingestion documentations.</p>
    pub fn set_partition_spec(mut self, input: ::std::option::Option<crate::types::DataLakeDatasetPartitionSpec>) -> Self {
        self.inner = self.inner.set_partition_spec(input);
        self
    }
    /// <p>The partition specification of the dataset. Partitioning can effectively improve the dataset query performance by reducing the amount of data scanned during query execution. But partitioning or not will affect how data get ingested by data ingestion methods, such as SendDataIntegrationEvent's dataset UPSERT will upsert records within partition (instead of within whole dataset). For more details, refer to those data ingestion documentations.</p>
    pub fn get_partition_spec(&self) -> &::std::option::Option<crate::types::DataLakeDatasetPartitionSpec> {
        self.inner.get_partition_spec()
    }
    ///
    /// Adds a key-value pair to `tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>The tags of the dataset.</p>
    pub fn tags(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.tags(k.into(), v.into());
        self
    }
    /// <p>The tags of the dataset.</p>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>The tags of the dataset.</p>
    pub fn get_tags(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
        self.inner.get_tags()
    }
}