#[non_exhaustive]pub struct CreateDatasetInputBuilder { /* private fields */ }
Expand description
A builder for CreateDatasetInput
.
Implementations§
source§impl CreateDatasetInputBuilder
impl CreateDatasetInputBuilder
sourcepub fn dataset_name(self, input: impl Into<String>) -> Self
pub fn dataset_name(self, input: impl Into<String>) -> Self
A name for the dataset.
This field is required.sourcepub fn set_dataset_name(self, input: Option<String>) -> Self
pub fn set_dataset_name(self, input: Option<String>) -> Self
A name for the dataset.
sourcepub fn get_dataset_name(&self) -> &Option<String>
pub fn get_dataset_name(&self) -> &Option<String>
A name for the dataset.
sourcepub fn domain(self, input: Domain) -> Self
pub fn domain(self, input: Domain) -> Self
The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain
parameter of the CreateDatasetGroup operation must match.
The Domain
and DatasetType
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 RETAIL
domain and TARGET_TIME_SERIES
as the DatasetType
, Amazon Forecast requires item_id
, timestamp
, and demand
fields to be present in your data. For more information, see Importing datasets.
sourcepub fn set_domain(self, input: Option<Domain>) -> Self
pub fn set_domain(self, input: Option<Domain>) -> Self
The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain
parameter of the CreateDatasetGroup operation must match.
The Domain
and DatasetType
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 RETAIL
domain and TARGET_TIME_SERIES
as the DatasetType
, Amazon Forecast requires item_id
, timestamp
, and demand
fields to be present in your data. For more information, see Importing datasets.
sourcepub fn get_domain(&self) -> &Option<Domain>
pub fn get_domain(&self) -> &Option<Domain>
The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain
parameter of the CreateDatasetGroup operation must match.
The Domain
and DatasetType
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 RETAIL
domain and TARGET_TIME_SERIES
as the DatasetType
, Amazon Forecast requires item_id
, timestamp
, and demand
fields to be present in your data. For more information, see Importing datasets.
sourcepub fn dataset_type(self, input: DatasetType) -> Self
pub fn dataset_type(self, input: DatasetType) -> Self
The dataset type. Valid values depend on the chosen Domain
.
sourcepub fn set_dataset_type(self, input: Option<DatasetType>) -> Self
pub fn set_dataset_type(self, input: Option<DatasetType>) -> Self
The dataset type. Valid values depend on the chosen Domain
.
sourcepub fn get_dataset_type(&self) -> &Option<DatasetType>
pub fn get_dataset_type(&self) -> &Option<DatasetType>
The dataset type. Valid values depend on the chosen Domain
.
sourcepub fn data_frequency(self, input: impl Into<String>) -> Self
pub fn data_frequency(self, input: impl Into<String>) -> Self
The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
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:
-
Minute - 1-59
-
Hour - 1-23
-
Day - 1-6
-
Week - 1-4
-
Month - 1-11
-
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
sourcepub fn set_data_frequency(self, input: Option<String>) -> Self
pub fn set_data_frequency(self, input: Option<String>) -> Self
The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
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:
-
Minute - 1-59
-
Hour - 1-23
-
Day - 1-6
-
Week - 1-4
-
Month - 1-11
-
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
sourcepub fn get_data_frequency(&self) -> &Option<String>
pub fn get_data_frequency(&self) -> &Option<String>
The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
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:
-
Minute - 1-59
-
Hour - 1-23
-
Day - 1-6
-
Week - 1-4
-
Month - 1-11
-
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
sourcepub fn schema(self, input: Schema) -> Self
pub fn schema(self, input: Schema) -> Self
The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain
and DatasetType
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 Dataset Domains and Dataset Types.
sourcepub fn set_schema(self, input: Option<Schema>) -> Self
pub fn set_schema(self, input: Option<Schema>) -> Self
The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain
and DatasetType
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 Dataset Domains and Dataset Types.
sourcepub fn get_schema(&self) -> &Option<Schema>
pub fn get_schema(&self) -> &Option<Schema>
The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain
and DatasetType
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 Dataset Domains and Dataset Types.
sourcepub fn encryption_config(self, input: EncryptionConfig) -> Self
pub fn encryption_config(self, input: EncryptionConfig) -> Self
An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
sourcepub fn set_encryption_config(self, input: Option<EncryptionConfig>) -> Self
pub fn set_encryption_config(self, input: Option<EncryptionConfig>) -> Self
An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
sourcepub fn get_encryption_config(&self) -> &Option<EncryptionConfig>
pub fn get_encryption_config(&self) -> &Option<EncryptionConfig>
An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
Appends an item to tags
.
To override the contents of this collection use set_tags
.
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.
The following basic restrictions apply to tags:
-
Maximum number of tags per resource - 50.
-
For each resource, each tag key must be unique, and each tag key can have only one value.
-
Maximum key length - 128 Unicode characters in UTF-8.
-
Maximum value length - 256 Unicode characters in UTF-8.
-
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: + - = . _ : / @.
-
Tag keys and values are case sensitive.
-
Do not use
aws:
,AWS:
, 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 hasaws
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 ofaws
do not count against your tags per resource limit.
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.
The following basic restrictions apply to tags:
-
Maximum number of tags per resource - 50.
-
For each resource, each tag key must be unique, and each tag key can have only one value.
-
Maximum key length - 128 Unicode characters in UTF-8.
-
Maximum value length - 256 Unicode characters in UTF-8.
-
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: + - = . _ : / @.
-
Tag keys and values are case sensitive.
-
Do not use
aws:
,AWS:
, 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 hasaws
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 ofaws
do not count against your tags per resource limit.
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.
The following basic restrictions apply to tags:
-
Maximum number of tags per resource - 50.
-
For each resource, each tag key must be unique, and each tag key can have only one value.
-
Maximum key length - 128 Unicode characters in UTF-8.
-
Maximum value length - 256 Unicode characters in UTF-8.
-
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: + - = . _ : / @.
-
Tag keys and values are case sensitive.
-
Do not use
aws:
,AWS:
, 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 hasaws
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 ofaws
do not count against your tags per resource limit.
sourcepub fn build(self) -> Result<CreateDatasetInput, BuildError>
pub fn build(self) -> Result<CreateDatasetInput, BuildError>
Consumes the builder and constructs a CreateDatasetInput
.
source§impl CreateDatasetInputBuilder
impl CreateDatasetInputBuilder
sourcepub async fn send_with(
self,
client: &Client
) -> Result<CreateDatasetOutput, SdkError<CreateDatasetError, HttpResponse>>
pub async fn send_with( self, client: &Client ) -> Result<CreateDatasetOutput, SdkError<CreateDatasetError, HttpResponse>>
Sends a request with this input using the given client.
Trait Implementations§
source§impl Clone for CreateDatasetInputBuilder
impl Clone for CreateDatasetInputBuilder
source§fn clone(&self) -> CreateDatasetInputBuilder
fn clone(&self) -> CreateDatasetInputBuilder
1.0.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moresource§impl Debug for CreateDatasetInputBuilder
impl Debug for CreateDatasetInputBuilder
source§impl Default for CreateDatasetInputBuilder
impl Default for CreateDatasetInputBuilder
source§fn default() -> CreateDatasetInputBuilder
fn default() -> CreateDatasetInputBuilder
source§impl PartialEq for CreateDatasetInputBuilder
impl PartialEq for CreateDatasetInputBuilder
source§fn eq(&self, other: &CreateDatasetInputBuilder) -> bool
fn eq(&self, other: &CreateDatasetInputBuilder) -> bool
self
and other
values to be equal, and is used
by ==
.impl StructuralPartialEq for CreateDatasetInputBuilder
Auto Trait Implementations§
impl Freeze for CreateDatasetInputBuilder
impl RefUnwindSafe for CreateDatasetInputBuilder
impl Send for CreateDatasetInputBuilder
impl Sync for CreateDatasetInputBuilder
impl Unpin for CreateDatasetInputBuilder
impl UnwindSafe for CreateDatasetInputBuilder
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
source§impl<T> Instrument for T
impl<T> Instrument for T
source§fn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
source§fn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
source§impl<T> IntoEither for T
impl<T> IntoEither for T
source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left
is true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read moresource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
self
into a Left
variant of Either<Self, Self>
if into_left(&self)
returns true
.
Converts self
into a Right
variant of Either<Self, Self>
otherwise. Read more