#[non_exhaustive]
pub struct CreateDatasetImportJobInput { pub job_name: Option<String>, pub dataset_arn: Option<String>, pub data_source: Option<DataSource>, pub role_arn: Option<String>, pub tags: Option<Vec<Tag>>, pub import_mode: Option<ImportMode>, pub publish_attribution_metrics_to_s3: Option<bool>, }

Fields (Non-exhaustive)§

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
§job_name: Option<String>

The name for the dataset import job.

§dataset_arn: Option<String>

The ARN of the dataset that receives the imported data.

§data_source: Option<DataSource>

The Amazon S3 bucket that contains the training data to import.

§role_arn: Option<String>

The ARN of the IAM role that has permissions to read from the Amazon S3 data source.

§tags: Option<Vec<Tag>>

A list of tags to apply to the dataset import job.

§import_mode: Option<ImportMode>

Specify how to add the new records to an existing dataset. The default import mode is FULL. If you haven't imported bulk records into the dataset previously, you can only specify FULL.

  • Specify FULL to overwrite all existing bulk data in your dataset. Data you imported individually is not replaced.

  • Specify INCREMENTAL to append the new records to the existing data in your dataset. Amazon Personalize replaces any record with the same ID with the new one.

§publish_attribution_metrics_to_s3: Option<bool>

If you created a metric attribution, specify whether to publish metrics for this import job to Amazon S3

Implementations§

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impl CreateDatasetImportJobInput

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pub fn job_name(&self) -> Option<&str>

The name for the dataset import job.

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pub fn dataset_arn(&self) -> Option<&str>

The ARN of the dataset that receives the imported data.

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pub fn data_source(&self) -> Option<&DataSource>

The Amazon S3 bucket that contains the training data to import.

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pub fn role_arn(&self) -> Option<&str>

The ARN of the IAM role that has permissions to read from the Amazon S3 data source.

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pub fn tags(&self) -> &[Tag]

A list of tags to apply to the dataset import job.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

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pub fn import_mode(&self) -> Option<&ImportMode>

Specify how to add the new records to an existing dataset. The default import mode is FULL. If you haven't imported bulk records into the dataset previously, you can only specify FULL.

  • Specify FULL to overwrite all existing bulk data in your dataset. Data you imported individually is not replaced.

  • Specify INCREMENTAL to append the new records to the existing data in your dataset. Amazon Personalize replaces any record with the same ID with the new one.

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pub fn publish_attribution_metrics_to_s3(&self) -> Option<bool>

If you created a metric attribution, specify whether to publish metrics for this import job to Amazon S3

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impl CreateDatasetImportJobInput

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pub fn builder() -> CreateDatasetImportJobInputBuilder

Creates a new builder-style object to manufacture CreateDatasetImportJobInput.

Trait Implementations§

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impl Clone for CreateDatasetImportJobInput

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fn clone(&self) -> CreateDatasetImportJobInput

Returns a copy of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for CreateDatasetImportJobInput

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl PartialEq for CreateDatasetImportJobInput

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fn eq(&self, other: &CreateDatasetImportJobInput) -> bool

This method tests for self and other values to be equal, and is used by ==.
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fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
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impl StructuralPartialEq for CreateDatasetImportJobInput

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