aws_sdk_forecast/operation/create_dataset/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_dataset::_create_dataset_output::CreateDatasetOutputBuilder;

pub use crate::operation::create_dataset::_create_dataset_input::CreateDatasetInputBuilder;

impl crate::operation::create_dataset::builders::CreateDatasetInputBuilder {
    /// 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_dataset::CreateDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_dataset::CreateDatasetError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.create_dataset();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `CreateDataset`.
///
/// <p>Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:</p>
/// <ul>
/// <li>
/// <p><i> <code>DataFrequency</code> </i> - How frequently your historical time-series data is collected.</p></li>
/// <li>
/// <p><i> <code>Domain</code> </i> and <i> <code>DatasetType</code> </i> - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields.</p></li>
/// <li>
/// <p><i> <code>Schema</code> </i> - A schema specifies the fields in the dataset, including the field name and data type.</p></li>
/// </ul>
/// <p>After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html">Importing datasets</a>.</p>
/// <p>To get a list of all your datasets, use the <a href="https://docs.aws.amazon.com/forecast/latest/dg/API_ListDatasets.html">ListDatasets</a> operation.</p>
/// <p>For example Forecast datasets, see the <a href="https://github.com/aws-samples/amazon-forecast-samples">Amazon Forecast Sample GitHub repository</a>.</p><note>
/// <p>The <code>Status</code> of a dataset must be <code>ACTIVE</code> before you can import training data. Use the <a href="https://docs.aws.amazon.com/forecast/latest/dg/API_DescribeDataset.html">DescribeDataset</a> operation to get the status.</p>
/// </note>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateDatasetFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::create_dataset::builders::CreateDatasetInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::create_dataset::CreateDatasetOutput,
        crate::operation::create_dataset::CreateDatasetError,
    > for CreateDatasetFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::create_dataset::CreateDatasetOutput,
            crate::operation::create_dataset::CreateDatasetError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl CreateDatasetFluentBuilder {
    /// Creates a new `CreateDatasetFluentBuilder`.
    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 CreateDataset as a reference.
    pub fn as_input(&self) -> &crate::operation::create_dataset::builders::CreateDatasetInputBuilder {
        &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_dataset::CreateDatasetOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::create_dataset::CreateDatasetError,
            ::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_dataset::CreateDataset::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::create_dataset::CreateDataset::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_dataset::CreateDatasetOutput,
        crate::operation::create_dataset::CreateDatasetError,
        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>A name for the dataset.</p>
    pub fn dataset_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.dataset_name(input.into());
        self
    }
    /// <p>A name for the dataset.</p>
    pub fn set_dataset_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_dataset_name(input);
        self
    }
    /// <p>A name for the dataset.</p>
    pub fn get_dataset_name(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_dataset_name()
    }
    /// <p>The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the <code>Domain</code> parameter of the <a href="https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html">CreateDatasetGroup</a> operation must match.</p>
    /// <p>The <code>Domain</code> and <code>DatasetType</code> that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the <code>RETAIL</code> domain and <code>TARGET_TIME_SERIES</code> as the <code>DatasetType</code>, Amazon Forecast requires <code>item_id</code>, <code>timestamp</code>, and <code>demand</code> fields to be present in your data. For more information, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html">Importing datasets</a>.</p>
    pub fn domain(mut self, input: crate::types::Domain) -> Self {
        self.inner = self.inner.domain(input);
        self
    }
    /// <p>The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the <code>Domain</code> parameter of the <a href="https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html">CreateDatasetGroup</a> operation must match.</p>
    /// <p>The <code>Domain</code> and <code>DatasetType</code> that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the <code>RETAIL</code> domain and <code>TARGET_TIME_SERIES</code> as the <code>DatasetType</code>, Amazon Forecast requires <code>item_id</code>, <code>timestamp</code>, and <code>demand</code> fields to be present in your data. For more information, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html">Importing datasets</a>.</p>
    pub fn set_domain(mut self, input: ::std::option::Option<crate::types::Domain>) -> Self {
        self.inner = self.inner.set_domain(input);
        self
    }
    /// <p>The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the <code>Domain</code> parameter of the <a href="https://docs.aws.amazon.com/forecast/latest/dg/API_CreateDatasetGroup.html">CreateDatasetGroup</a> operation must match.</p>
    /// <p>The <code>Domain</code> and <code>DatasetType</code> that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the <code>RETAIL</code> domain and <code>TARGET_TIME_SERIES</code> as the <code>DatasetType</code>, Amazon Forecast requires <code>item_id</code>, <code>timestamp</code>, and <code>demand</code> fields to be present in your data. For more information, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-datasets-groups.html">Importing datasets</a>.</p>
    pub fn get_domain(&self) -> &::std::option::Option<crate::types::Domain> {
        self.inner.get_domain()
    }
    /// <p>The dataset type. Valid values depend on the chosen <code>Domain</code>.</p>
    pub fn dataset_type(mut self, input: crate::types::DatasetType) -> Self {
        self.inner = self.inner.dataset_type(input);
        self
    }
    /// <p>The dataset type. Valid values depend on the chosen <code>Domain</code>.</p>
    pub fn set_dataset_type(mut self, input: ::std::option::Option<crate::types::DatasetType>) -> Self {
        self.inner = self.inner.set_dataset_type(input);
        self
    }
    /// <p>The dataset type. Valid values depend on the chosen <code>Domain</code>.</p>
    pub fn get_dataset_type(&self) -> &::std::option::Option<crate::types::DatasetType> {
        self.inner.get_dataset_type()
    }
    /// <p>The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.</p>
    /// <p>Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:</p>
    /// <ul>
    /// <li>
    /// <p>Minute - 1-59</p></li>
    /// <li>
    /// <p>Hour - 1-23</p></li>
    /// <li>
    /// <p>Day - 1-6</p></li>
    /// <li>
    /// <p>Week - 1-4</p></li>
    /// <li>
    /// <p>Month - 1-11</p></li>
    /// <li>
    /// <p>Year - 1</p></li>
    /// </ul>
    /// <p>Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".</p>
    pub fn data_frequency(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.data_frequency(input.into());
        self
    }
    /// <p>The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.</p>
    /// <p>Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:</p>
    /// <ul>
    /// <li>
    /// <p>Minute - 1-59</p></li>
    /// <li>
    /// <p>Hour - 1-23</p></li>
    /// <li>
    /// <p>Day - 1-6</p></li>
    /// <li>
    /// <p>Week - 1-4</p></li>
    /// <li>
    /// <p>Month - 1-11</p></li>
    /// <li>
    /// <p>Year - 1</p></li>
    /// </ul>
    /// <p>Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".</p>
    pub fn set_data_frequency(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_data_frequency(input);
        self
    }
    /// <p>The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.</p>
    /// <p>Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:</p>
    /// <ul>
    /// <li>
    /// <p>Minute - 1-59</p></li>
    /// <li>
    /// <p>Hour - 1-23</p></li>
    /// <li>
    /// <p>Day - 1-6</p></li>
    /// <li>
    /// <p>Week - 1-4</p></li>
    /// <li>
    /// <p>Month - 1-11</p></li>
    /// <li>
    /// <p>Year - 1</p></li>
    /// </ul>
    /// <p>Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".</p>
    pub fn get_data_frequency(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_data_frequency()
    }
    /// <p>The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset <code>Domain</code> and <code>DatasetType</code> that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-domains-ds-types.html">Dataset Domains and Dataset Types</a>.</p>
    pub fn schema(mut self, input: crate::types::Schema) -> Self {
        self.inner = self.inner.schema(input);
        self
    }
    /// <p>The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset <code>Domain</code> and <code>DatasetType</code> that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-domains-ds-types.html">Dataset Domains and Dataset Types</a>.</p>
    pub fn set_schema(mut self, input: ::std::option::Option<crate::types::Schema>) -> Self {
        self.inner = self.inner.set_schema(input);
        self
    }
    /// <p>The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset <code>Domain</code> and <code>DatasetType</code> that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see <a href="https://docs.aws.amazon.com/forecast/latest/dg/howitworks-domains-ds-types.html">Dataset Domains and Dataset Types</a>.</p>
    pub fn get_schema(&self) -> &::std::option::Option<crate::types::Schema> {
        self.inner.get_schema()
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn encryption_config(mut self, input: crate::types::EncryptionConfig) -> Self {
        self.inner = self.inner.encryption_config(input);
        self
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn set_encryption_config(mut self, input: ::std::option::Option<crate::types::EncryptionConfig>) -> Self {
        self.inner = self.inner.set_encryption_config(input);
        self
    }
    /// <p>An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.</p>
    pub fn get_encryption_config(&self) -> &::std::option::Option<crate::types::EncryptionConfig> {
        self.inner.get_encryption_config()
    }
    ///
    /// Appends an item to `Tags`.
    ///
    /// To override the contents of this collection use [`set_tags`](Self::set_tags).
    ///
    /// <p>The optional metadata that you apply to the dataset to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn tags(mut self, input: crate::types::Tag) -> Self {
        self.inner = self.inner.tags(input);
        self
    }
    /// <p>The optional metadata that you apply to the dataset to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
        self.inner = self.inner.set_tags(input);
        self
    }
    /// <p>The optional metadata that you apply to the dataset to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.</p>
    /// <p>The following basic restrictions apply to tags:</p>
    /// <ul>
    /// <li>
    /// <p>Maximum number of tags per resource - 50.</p></li>
    /// <li>
    /// <p>For each resource, each tag key must be unique, and each tag key can have only one value.</p></li>
    /// <li>
    /// <p>Maximum key length - 128 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>Maximum value length - 256 Unicode characters in UTF-8.</p></li>
    /// <li>
    /// <p>If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.</p></li>
    /// <li>
    /// <p>Tag keys and values are case sensitive.</p></li>
    /// <li>
    /// <p>Do not use <code>aws:</code>, <code>AWS:</code>, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has <code>aws</code> as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of <code>aws</code> do not count against your tags per resource limit.</p></li>
    /// </ul>
    pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
        self.inner.get_tags()
    }
}