Struct aws_sdk_personalize::Client

source ·

pub struct Client { /* private fields */ }

Expand description

Client for Amazon Personalize

Client for invoking operations on Amazon Personalize. Each operation on Amazon Personalize is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Examples

Constructing a client and invoking an operation

    // create a shared configuration. This can be used & shared between multiple service clients.
    let shared_config = aws_config::load_from_env().await;
    let client = aws_sdk_personalize::Client::new(&shared_config);
    // invoke an operation
    /* let rsp = client
        .<operation_name>().
        .<param>("some value")
        .send().await; */

Constructing a client with custom configuration

use aws_config::retry::RetryConfig;
let shared_config = aws_config::load_from_env().await;
let config = aws_sdk_personalize::config::Builder::from(&shared_config)
  .retry_config(RetryConfig::disabled())
  .build();
let client = aws_sdk_personalize::Client::from_conf(config);

Implementations§

source §

impl Client

source

pub fn with_config(
client: Client<DynConnector, DynMiddleware<DynConnector>>,
conf: Config
) -> Self

Creates a client with the given service configuration.

source

pub fn conf(&self) -> &Config

Returns the client’s configuration.

source §

impl Client

source

pub fn create_batch_inference_job(&self) -> CreateBatchInferenceJob

Constructs a fluent builder for the CreateBatchInferenceJob operation.

The fluent builder is configurable:
- job_name(impl Into<String>) / set_job_name(Option<String>):
 The name of the batch inference job to create.
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version that will be used to generate the batch inference recommendations.
- filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
 The ARN of the filter to apply to the batch inference job. For more information on using filters, see Filtering batch recommendations.
- num_results(i32) / set_num_results(Option<i32>):
 The number of recommendations to retrieve.
- job_input(BatchInferenceJobInput) / set_job_input(Option<BatchInferenceJobInput>):
 The Amazon S3 path that leads to the input file to base your recommendations on. The input material must be in JSON format.
- job_output(BatchInferenceJobOutput) / set_job_output(Option<BatchInferenceJobOutput>):
 The path to the Amazon S3 bucket where the job’s output will be stored.
- role_arn(impl Into<String>) / set_role_arn(Option<String>):
 The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and output Amazon S3 buckets respectively.
- batch_inference_job_config(BatchInferenceJobConfig) / set_batch_inference_job_config(Option<BatchInferenceJobConfig>):
 The configuration details of a batch inference job.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the batch inference job.
On success, responds with CreateBatchInferenceJobOutput with field(s):
- batch_inference_job_arn(Option<String>):
 The ARN of the batch inference job.
On failure, responds with SdkError<CreateBatchInferenceJobError>

source

pub fn create_batch_segment_job(&self) -> CreateBatchSegmentJob

Constructs a fluent builder for the CreateBatchSegmentJob operation.

The fluent builder is configurable:
- job_name(impl Into<String>) / set_job_name(Option<String>):
 The name of the batch segment job to create.
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version you want the batch segment job to use to generate batch segments.
- filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
 The ARN of the filter to apply to the batch segment job. For more information on using filters, see Filtering batch recommendations.
- num_results(i32) / set_num_results(Option<i32>):
 The number of predicted users generated by the batch segment job for each line of input data.
- job_input(BatchSegmentJobInput) / set_job_input(Option<BatchSegmentJobInput>):
 The Amazon S3 path for the input data used to generate the batch segment job.
- job_output(BatchSegmentJobOutput) / set_job_output(Option<BatchSegmentJobOutput>):
 The Amazon S3 path for the bucket where the job’s output will be stored.
- role_arn(impl Into<String>) / set_role_arn(Option<String>):
 The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and output Amazon S3 buckets respectively.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the batch segment job.
On success, responds with CreateBatchSegmentJobOutput with field(s):
- batch_segment_job_arn(Option<String>):
 The ARN of the batch segment job.
On failure, responds with SdkError<CreateBatchSegmentJobError>

source

pub fn create_campaign(&self) -> CreateCampaign

Constructs a fluent builder for the CreateCampaign operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 A name for the new campaign. The campaign name must be unique within your account.
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version to deploy.
- min_provisioned_tps(i32) / set_min_provisioned_tps(Option<i32>):
 Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
- campaign_config(CampaignConfig) / set_campaign_config(Option<CampaignConfig>):
 The configuration details of a campaign.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the campaign.
On success, responds with CreateCampaignOutput with field(s):
- campaign_arn(Option<String>):
 The Amazon Resource Name (ARN) of the campaign.
On failure, responds with SdkError<CreateCampaignError>

source

pub fn create_dataset(&self) -> CreateDataset

Constructs a fluent builder for the CreateDataset operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name for the dataset.
- schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
 The ARN of the schema to associate with the dataset. The schema defines the dataset fields.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group to add the dataset to.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of dataset.
 
 One of the following (case insensitive) values:
 - Interactions
 - Items
 - Users
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the dataset.
On success, responds with CreateDatasetOutput with field(s):
- dataset_arn(Option<String>):
 The ARN of the dataset.
On failure, responds with SdkError<CreateDatasetError>

source

pub fn create_dataset_export_job(&self) -> CreateDatasetExportJob

Constructs a fluent builder for the CreateDatasetExportJob operation.

The fluent builder is configurable:
- job_name(impl Into<String>) / set_job_name(Option<String>):
 The name for the dataset export job.
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset that contains the data to export.
- ingestion_mode(IngestionMode) / set_ingestion_mode(Option<IngestionMode>):
 The data to export, based on how you imported the data. You can choose to export only BULK data that you imported using a dataset import job, only PUT data that you imported incrementally (using the console, PutEvents, PutUsers and PutItems operations), or ALL for both types. The default value is PUT.
- role_arn(impl Into<String>) / set_role_arn(Option<String>):
 The Amazon Resource Name (ARN) of the IAM service role that has permissions to add data to your output Amazon S3 bucket.
- job_output(DatasetExportJobOutput) / set_job_output(Option<DatasetExportJobOutput>):
 The path to the Amazon S3 bucket where the job’s output is stored.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the dataset export job.
On success, responds with CreateDatasetExportJobOutput with field(s):
- dataset_export_job_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset export job.
On failure, responds with SdkError<CreateDatasetExportJobError>

source

pub fn create_dataset_group(&self) -> CreateDatasetGroup

Constructs a fluent builder for the CreateDatasetGroup operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name for the new dataset group.
- role_arn(impl Into<String>) / set_role_arn(Option<String>):
 The ARN of the Identity and Access Management (IAM) role that has permissions to access the Key Management Service (KMS) key. Supplying an IAM role is only valid when also specifying a KMS key.
- kms_key_arn(impl Into<String>) / set_kms_key_arn(Option<String>):
 The Amazon Resource Name (ARN) of a Key Management Service (KMS) key used to encrypt the datasets.
- domain(Domain) / set_domain(Option<Domain>):
 The domain of the dataset group. Specify a domain to create a Domain dataset group. The domain you specify determines the default schemas for datasets and the use cases available for recommenders. If you don’t specify a domain, you create a Custom dataset group with solution versions that you deploy with a campaign.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the dataset group.
On success, responds with CreateDatasetGroupOutput with field(s):
- dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the new dataset group.
- domain(Option<Domain>):
 The domain for the new Domain dataset group.
On failure, responds with SdkError<CreateDatasetGroupError>

source

pub fn create_dataset_import_job(&self) -> CreateDatasetImportJob

Constructs a fluent builder for the CreateDatasetImportJob operation.

The fluent builder is configurable:
- job_name(impl Into<String>) / set_job_name(Option<String>):
 The name for the dataset import job.
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The ARN of the dataset that receives the imported data.
- data_source(DataSource) / set_data_source(Option<DataSource>):
 The Amazon S3 bucket that contains the training data to import.
- role_arn(impl Into<String>) / set_role_arn(Option<String>):
 The ARN of the IAM role that has permissions to read from the Amazon S3 data source.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the dataset import job.
- import_mode(ImportMode) / set_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(bool) / set_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
On success, responds with CreateDatasetImportJobOutput with field(s):
- dataset_import_job_arn(Option<String>):
 The ARN of the dataset import job.
On failure, responds with SdkError<CreateDatasetImportJobError>

source

pub fn create_event_tracker(&self) -> CreateEventTracker

Constructs a fluent builder for the CreateEventTracker operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name for the event tracker.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group that receives the event data.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the event tracker.
On success, responds with CreateEventTrackerOutput with field(s):
- event_tracker_arn(Option<String>):
 The ARN of the event tracker.
- tracking_id(Option<String>):
 The ID of the event tracker. Include this ID in requests to the PutEvents API.
On failure, responds with SdkError<CreateEventTrackerError>

source

pub fn create_filter(&self) -> CreateFilter

Constructs a fluent builder for the CreateFilter operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name of the filter to create.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The ARN of the dataset group that the filter will belong to.
- filter_expression(impl Into<String>) / set_filter_expression(Option<String>):
 The filter expression defines which items are included or excluded from recommendations. Filter expression must follow specific format rules. For information about filter expression structure and syntax, see Filter expressions.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the filter.
On success, responds with CreateFilterOutput with field(s):
- filter_arn(Option<String>):
 The ARN of the new filter.
On failure, responds with SdkError<CreateFilterError>

source

pub fn create_metric_attribution(&self) -> CreateMetricAttribution

Constructs a fluent builder for the CreateMetricAttribution operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 A name for the metric attribution.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the destination dataset group for the metric attribution.
- metrics(Vec<MetricAttribute>) / set_metrics(Option<Vec<MetricAttribute>>):
 A list of metric attributes for the metric attribution. Each metric attribute specifies an event type to track and a function. Available functions are SUM() or SAMPLECOUNT(). For SUM() functions, provide the dataset type (either Interactions or Items) and column to sum as a parameter. For example SUM(Items.PRICE).
- metrics_output_config(MetricAttributionOutput) / set_metrics_output_config(Option<MetricAttributionOutput>):
 The output configuration details for the metric attribution.
On success, responds with CreateMetricAttributionOutput with field(s):
- metric_attribution_arn(Option<String>):
 The Amazon Resource Name (ARN) for the new metric attribution.
On failure, responds with SdkError<CreateMetricAttributionError>

source

pub fn create_recommender(&self) -> CreateRecommender

Constructs a fluent builder for the CreateRecommender operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name of the recommender.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the destination domain dataset group for the recommender.
- recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recipe that the recommender will use. For a recommender, a recipe is a Domain dataset group use case. Only Domain dataset group use cases can be used to create a recommender. For information about use cases see Choosing recommender use cases.
- recommender_config(RecommenderConfig) / set_recommender_config(Option<RecommenderConfig>):
 The configuration details of the recommender.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the recommender.
On success, responds with CreateRecommenderOutput with field(s):
- recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender.
On failure, responds with SdkError<CreateRecommenderError>

source

pub fn create_schema(&self) -> CreateSchema

Constructs a fluent builder for the CreateSchema operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name for the schema.
- schema(impl Into<String>) / set_schema(Option<String>):
 A schema in Avro JSON format.
- domain(Domain) / set_domain(Option<Domain>):
 The domain for the schema. If you are creating a schema for a dataset in a Domain dataset group, specify the domain you chose when you created the Domain dataset group.
On success, responds with CreateSchemaOutput with field(s):
- schema_arn(Option<String>):
 The Amazon Resource Name (ARN) of the created schema.
On failure, responds with SdkError<CreateSchemaError>

source

pub fn create_solution(&self) -> CreateSolution

Constructs a fluent builder for the CreateSolution operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name for the solution.
- perform_hpo(bool) / set_perform_hpo(Option<bool>):
 Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false.
 
 When performing AutoML, this parameter is always true and you should not set it to false.
- perform_auto_ml(bool) / set_perform_auto_ml(bool):
 Whether to perform automated machine learning (AutoML). The default is false. For this case, you must specify recipeArn.
 
 When set to true, Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn. Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.
- recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
 The ARN of the recipe to use for model training. Only specified when performAutoML is false.
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group that provides the training data.
- event_type(impl Into<String>) / set_event_type(Option<String>):
 When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, ‘click’ or ‘like’) is used for training the model.
 
 If you do not provide an eventType, Amazon Personalize will use all interactions for training with equal weight regardless of type.
- solution_config(SolutionConfig) / set_solution_config(Option<SolutionConfig>):
 The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
 
 Amazon Personalize doesn’t support configuring the hpoObjective at this time.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the solution.
On success, responds with CreateSolutionOutput with field(s):
- solution_arn(Option<String>):
 The ARN of the solution.
On failure, responds with SdkError<CreateSolutionError>

source

pub fn create_solution_version(&self) -> CreateSolutionVersion

Constructs a fluent builder for the CreateSolutionVersion operation.

The fluent builder is configurable:
- name(impl Into<String>) / set_name(Option<String>):
 The name of the solution version.
- solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution containing the training configuration information.
- training_mode(TrainingMode) / set_training_mode(Option<TrainingMode>):
 The scope of training to be performed when creating the solution version. The FULL option trains the solution version based on the entirety of the input solution’s training data, while the UPDATE option processes only the data that has changed in comparison to the input solution. Choose UPDATE when you want to incrementally update your solution version instead of creating an entirely new one.
 
 The UPDATE option can only be used when you already have an active solution version created from the input solution using the FULL option and the input solution was trained with the User-Personalization recipe or the HRNN-Coldstart recipe.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A list of tags to apply to the solution version.
On success, responds with CreateSolutionVersionOutput with field(s):
- solution_version_arn(Option<String>):
 The ARN of the new solution version.
On failure, responds with SdkError<CreateSolutionVersionError>

source

pub fn delete_campaign(&self) -> DeleteCampaign

Constructs a fluent builder for the DeleteCampaign operation.

The fluent builder is configurable:
- campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
 The Amazon Resource Name (ARN) of the campaign to delete.
On success, responds with DeleteCampaignOutput
On failure, responds with SdkError<DeleteCampaignError>

source

pub fn delete_dataset(&self) -> DeleteDataset

Constructs a fluent builder for the DeleteDataset operation.

The fluent builder is configurable:
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset to delete.
On success, responds with DeleteDatasetOutput
On failure, responds with SdkError<DeleteDatasetError>

source

pub fn delete_dataset_group(&self) -> DeleteDatasetGroup

Constructs a fluent builder for the DeleteDatasetGroup operation.

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The ARN of the dataset group to delete.
On success, responds with DeleteDatasetGroupOutput
On failure, responds with SdkError<DeleteDatasetGroupError>

source

pub fn delete_event_tracker(&self) -> DeleteEventTracker

Constructs a fluent builder for the DeleteEventTracker operation.

The fluent builder is configurable:
- event_tracker_arn(impl Into<String>) / set_event_tracker_arn(Option<String>):
 The Amazon Resource Name (ARN) of the event tracker to delete.
On success, responds with DeleteEventTrackerOutput
On failure, responds with SdkError<DeleteEventTrackerError>

source

pub fn delete_filter(&self) -> DeleteFilter

Constructs a fluent builder for the DeleteFilter operation.

The fluent builder is configurable:
- filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
 The ARN of the filter to delete.
On success, responds with DeleteFilterOutput
On failure, responds with SdkError<DeleteFilterError>

source

pub fn delete_metric_attribution(&self) -> DeleteMetricAttribution

Constructs a fluent builder for the DeleteMetricAttribution operation.

The fluent builder is configurable:
- metric_attribution_arn(impl Into<String>) / set_metric_attribution_arn(Option<String>):
 The metric attribution’s Amazon Resource Name (ARN).
On success, responds with DeleteMetricAttributionOutput
On failure, responds with SdkError<DeleteMetricAttributionError>

source

pub fn delete_recommender(&self) -> DeleteRecommender

Constructs a fluent builder for the DeleteRecommender operation.

The fluent builder is configurable:
- recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender to delete.
On success, responds with DeleteRecommenderOutput
On failure, responds with SdkError<DeleteRecommenderError>

source

pub fn delete_schema(&self) -> DeleteSchema

Constructs a fluent builder for the DeleteSchema operation.

The fluent builder is configurable:
- schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
 The Amazon Resource Name (ARN) of the schema to delete.
On success, responds with DeleteSchemaOutput
On failure, responds with SdkError<DeleteSchemaError>

source

pub fn delete_solution(&self) -> DeleteSolution

Constructs a fluent builder for the DeleteSolution operation.

The fluent builder is configurable:
- solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
 The ARN of the solution to delete.
On success, responds with DeleteSolutionOutput
On failure, responds with SdkError<DeleteSolutionError>

source

pub fn describe_algorithm(&self) -> DescribeAlgorithm

Constructs a fluent builder for the DescribeAlgorithm operation.

The fluent builder is configurable:
- algorithm_arn(impl Into<String>) / set_algorithm_arn(Option<String>):
 The Amazon Resource Name (ARN) of the algorithm to describe.
On success, responds with DescribeAlgorithmOutput with field(s):
- algorithm(Option<Algorithm>):
 A listing of the properties of the algorithm.
On failure, responds with SdkError<DescribeAlgorithmError>

source

pub fn describe_batch_inference_job(&self) -> DescribeBatchInferenceJob

Constructs a fluent builder for the DescribeBatchInferenceJob operation.

The fluent builder is configurable:
- batch_inference_job_arn(impl Into<String>) / set_batch_inference_job_arn(Option<String>):
 The ARN of the batch inference job to describe.
On success, responds with DescribeBatchInferenceJobOutput with field(s):
- batch_inference_job(Option<BatchInferenceJob>):
 Information on the specified batch inference job.
On failure, responds with SdkError<DescribeBatchInferenceJobError>

source

pub fn describe_batch_segment_job(&self) -> DescribeBatchSegmentJob

Constructs a fluent builder for the DescribeBatchSegmentJob operation.

The fluent builder is configurable:
- batch_segment_job_arn(impl Into<String>) / set_batch_segment_job_arn(Option<String>):
 The ARN of the batch segment job to describe.
On success, responds with DescribeBatchSegmentJobOutput with field(s):
- batch_segment_job(Option<BatchSegmentJob>):
 Information on the specified batch segment job.
On failure, responds with SdkError<DescribeBatchSegmentJobError>

source

pub fn describe_campaign(&self) -> DescribeCampaign

Constructs a fluent builder for the DescribeCampaign operation.

The fluent builder is configurable:
- campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
 The Amazon Resource Name (ARN) of the campaign.
On success, responds with DescribeCampaignOutput with field(s):
- campaign(Option<Campaign>):
 The properties of the campaign.
On failure, responds with SdkError<DescribeCampaignError>

source

pub fn describe_dataset(&self) -> DescribeDataset

Constructs a fluent builder for the DescribeDataset operation.

The fluent builder is configurable:
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset to describe.
On success, responds with DescribeDatasetOutput with field(s):
- dataset(Option<Dataset>):
 A listing of the dataset’s properties.
On failure, responds with SdkError<DescribeDatasetError>

source

pub fn describe_dataset_export_job(&self) -> DescribeDatasetExportJob

Constructs a fluent builder for the DescribeDatasetExportJob operation.

The fluent builder is configurable:
- dataset_export_job_arn(impl Into<String>) / set_dataset_export_job_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset export job to describe.
On success, responds with DescribeDatasetExportJobOutput with field(s):
- dataset_export_job(Option<DatasetExportJob>):
 Information about the dataset export job, including the status.
 
 The status is one of the following values:
 - CREATE PENDING
 - CREATE IN_PROGRESS
 - ACTIVE
 - CREATE FAILED
On failure, responds with SdkError<DescribeDatasetExportJobError>

source

pub fn describe_dataset_group(&self) -> DescribeDatasetGroup

Constructs a fluent builder for the DescribeDatasetGroup operation.

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group to describe.
On success, responds with DescribeDatasetGroupOutput with field(s):
- dataset_group(Option<DatasetGroup>):
 A listing of the dataset group’s properties.
On failure, responds with SdkError<DescribeDatasetGroupError>

source

pub fn describe_dataset_import_job(&self) -> DescribeDatasetImportJob

Constructs a fluent builder for the DescribeDatasetImportJob operation.

The fluent builder is configurable:
- dataset_import_job_arn(impl Into<String>) / set_dataset_import_job_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset import job to describe.
On success, responds with DescribeDatasetImportJobOutput with field(s):
- dataset_import_job(Option<DatasetImportJob>):
 Information about the dataset import job, including the status.
 
 The status is one of the following values:
 - CREATE PENDING
 - CREATE IN_PROGRESS
 - ACTIVE
 - CREATE FAILED
On failure, responds with SdkError<DescribeDatasetImportJobError>

source

pub fn describe_event_tracker(&self) -> DescribeEventTracker

Constructs a fluent builder for the DescribeEventTracker operation.

The fluent builder is configurable:
- event_tracker_arn(impl Into<String>) / set_event_tracker_arn(Option<String>):
 The Amazon Resource Name (ARN) of the event tracker to describe.
On success, responds with DescribeEventTrackerOutput with field(s):
- event_tracker(Option<EventTracker>):
 An object that describes the event tracker.
On failure, responds with SdkError<DescribeEventTrackerError>

source

pub fn describe_feature_transformation(&self) -> DescribeFeatureTransformation

Constructs a fluent builder for the DescribeFeatureTransformation operation.

The fluent builder is configurable:
- feature_transformation_arn(impl Into<String>) / set_feature_transformation_arn(Option<String>):
 The Amazon Resource Name (ARN) of the feature transformation to describe.
On success, responds with DescribeFeatureTransformationOutput with field(s):
- feature_transformation(Option<FeatureTransformation>):
 A listing of the FeatureTransformation properties.
On failure, responds with SdkError<DescribeFeatureTransformationError>

source

pub fn describe_filter(&self) -> DescribeFilter

Constructs a fluent builder for the DescribeFilter operation.

The fluent builder is configurable:
- filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
 The ARN of the filter to describe.
On success, responds with DescribeFilterOutput with field(s):
- filter(Option<Filter>):
 The filter’s details.
On failure, responds with SdkError<DescribeFilterError>

source

pub fn describe_metric_attribution(&self) -> DescribeMetricAttribution

Constructs a fluent builder for the DescribeMetricAttribution operation.

The fluent builder is configurable:
- metric_attribution_arn(impl Into<String>) / set_metric_attribution_arn(Option<String>):
 The metric attribution’s Amazon Resource Name (ARN).
On success, responds with DescribeMetricAttributionOutput with field(s):
- metric_attribution(Option<MetricAttribution>):
 The details of the metric attribution.
On failure, responds with SdkError<DescribeMetricAttributionError>

source

pub fn describe_recipe(&self) -> DescribeRecipe

Constructs a fluent builder for the DescribeRecipe operation.

The fluent builder is configurable:
- recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recipe to describe.
On success, responds with DescribeRecipeOutput with field(s):
- recipe(Option<Recipe>):
 An object that describes the recipe.
On failure, responds with SdkError<DescribeRecipeError>

source

pub fn describe_recommender(&self) -> DescribeRecommender

Constructs a fluent builder for the DescribeRecommender operation.

The fluent builder is configurable:
- recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender to describe.
On success, responds with DescribeRecommenderOutput with field(s):
- recommender(Option<Recommender>):
 The properties of the recommender.
On failure, responds with SdkError<DescribeRecommenderError>

source

pub fn describe_schema(&self) -> DescribeSchema

Constructs a fluent builder for the DescribeSchema operation.

The fluent builder is configurable:
- schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
 The Amazon Resource Name (ARN) of the schema to retrieve.
On success, responds with DescribeSchemaOutput with field(s):
- schema(Option<DatasetSchema>):
 The requested schema.
On failure, responds with SdkError<DescribeSchemaError>

source

pub fn describe_solution(&self) -> DescribeSolution

Constructs a fluent builder for the DescribeSolution operation.

The fluent builder is configurable:
- solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution to describe.
On success, responds with DescribeSolutionOutput with field(s):
- solution(Option<Solution>):
 An object that describes the solution.
On failure, responds with SdkError<DescribeSolutionError>

source

pub fn describe_solution_version(&self) -> DescribeSolutionVersion

Constructs a fluent builder for the DescribeSolutionVersion operation.

The fluent builder is configurable:
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version.
On success, responds with DescribeSolutionVersionOutput with field(s):
- solution_version(Option<SolutionVersion>):
 The solution version.
On failure, responds with SdkError<DescribeSolutionVersionError>

source

pub fn get_solution_metrics(&self) -> GetSolutionMetrics

Constructs a fluent builder for the GetSolutionMetrics operation.

The fluent builder is configurable:
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version for which to get metrics.
On success, responds with GetSolutionMetricsOutput with field(s):
- solution_version_arn(Option<String>):
 The same solution version ARN as specified in the request.
- metrics(Option<HashMap<String, f64>>):
 The metrics for the solution version. For more information, see Evaluating a solution version with metrics .
On failure, responds with SdkError<GetSolutionMetricsError>

source

pub fn list_batch_inference_jobs(&self) -> ListBatchInferenceJobs

Constructs a fluent builder for the ListBatchInferenceJobs operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version from which the batch inference jobs were created.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 The token to request the next page of results.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of batch inference job results to return in each page. The default value is 100.
On success, responds with ListBatchInferenceJobsOutput with field(s):
- batch_inference_jobs(Option<Vec<BatchInferenceJobSummary>>):
 A list containing information on each job that is returned.
- next_token(Option<String>):
 The token to use to retrieve the next page of results. The value is null when there are no more results to return.
On failure, responds with SdkError<ListBatchInferenceJobsError>

source

pub fn list_batch_segment_jobs(&self) -> ListBatchSegmentJobs

Constructs a fluent builder for the ListBatchSegmentJobs operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version that the batch segment jobs used to generate batch segments.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 The token to request the next page of results.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of batch segment job results to return in each page. The default value is 100.
On success, responds with ListBatchSegmentJobsOutput with field(s):
- batch_segment_jobs(Option<Vec<BatchSegmentJobSummary>>):
 A list containing information on each job that is returned.
- next_token(Option<String>):
 The token to use to retrieve the next page of results. The value is null when there are no more results to return.
On failure, responds with SdkError<ListBatchSegmentJobsError>

source

pub fn list_campaigns(&self) -> ListCampaigns

Constructs a fluent builder for the ListCampaigns operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution to list the campaigns for. When a solution is not specified, all the campaigns associated with the account are listed.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListCampaigns for getting the next set of campaigns (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of campaigns to return.
On success, responds with ListCampaignsOutput with field(s):
- campaigns(Option<Vec<CampaignSummary>>):
 A list of the campaigns.
- next_token(Option<String>):
 A token for getting the next set of campaigns (if they exist).
On failure, responds with SdkError<ListCampaignsError>

source

pub fn list_dataset_export_jobs(&self) -> ListDatasetExportJobs

Constructs a fluent builder for the ListDatasetExportJobs operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset to list the dataset export jobs for.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListDatasetExportJobs for getting the next set of dataset export jobs (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of dataset export jobs to return.
On success, responds with ListDatasetExportJobsOutput with field(s):
- dataset_export_jobs(Option<Vec<DatasetExportJobSummary>>):
 The list of dataset export jobs.
- next_token(Option<String>):
 A token for getting the next set of dataset export jobs (if they exist).
On failure, responds with SdkError<ListDatasetExportJobsError>

source

pub fn list_dataset_groups(&self) -> ListDatasetGroups

Constructs a fluent builder for the ListDatasetGroups operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListDatasetGroups for getting the next set of dataset groups (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of dataset groups to return.
On success, responds with ListDatasetGroupsOutput with field(s):
- dataset_groups(Option<Vec<DatasetGroupSummary>>):
 The list of your dataset groups.
- next_token(Option<String>):
 A token for getting the next set of dataset groups (if they exist).
On failure, responds with SdkError<ListDatasetGroupsError>

source

pub fn list_dataset_import_jobs(&self) -> ListDatasetImportJobs

Constructs a fluent builder for the ListDatasetImportJobs operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset to list the dataset import jobs for.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of dataset import jobs to return.
On success, responds with ListDatasetImportJobsOutput with field(s):
- dataset_import_jobs(Option<Vec<DatasetImportJobSummary>>):
 The list of dataset import jobs.
- next_token(Option<String>):
 A token for getting the next set of dataset import jobs (if they exist).
On failure, responds with SdkError<ListDatasetImportJobsError>

source

pub fn list_datasets(&self) -> ListDatasets

Constructs a fluent builder for the ListDatasets operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group that contains the datasets to list.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of datasets to return.
On success, responds with ListDatasetsOutput with field(s):
- datasets(Option<Vec<DatasetSummary>>):
 An array of Dataset objects. Each object provides metadata information.
- next_token(Option<String>):
 A token for getting the next set of datasets (if they exist).
On failure, responds with SdkError<ListDatasetsError>

source

pub fn list_event_trackers(&self) -> ListEventTrackers

Constructs a fluent builder for the ListEventTrackers operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The ARN of a dataset group used to filter the response.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListEventTrackers for getting the next set of event trackers (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of event trackers to return.
On success, responds with ListEventTrackersOutput with field(s):
- event_trackers(Option<Vec<EventTrackerSummary>>):
 A list of event trackers.
- next_token(Option<String>):
 A token for getting the next set of event trackers (if they exist).
On failure, responds with SdkError<ListEventTrackersError>

source

pub fn list_filters(&self) -> ListFilters

Constructs a fluent builder for the ListFilters operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The ARN of the dataset group that contains the filters.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListFilters for getting the next set of filters (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of filters to return.
On success, responds with ListFiltersOutput with field(s):
- filters(Option<Vec<FilterSummary>>):
 A list of returned filters.
- next_token(Option<String>):
 A token for getting the next set of filters (if they exist).
On failure, responds with SdkError<ListFiltersError>

source

pub fn list_metric_attribution_metrics(&self) -> ListMetricAttributionMetrics

Constructs a fluent builder for the ListMetricAttributionMetrics operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- metric_attribution_arn(impl Into<String>) / set_metric_attribution_arn(Option<String>):
 The Amazon Resource Name (ARN) of the metric attribution to retrieve attributes for.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 Specify the pagination token from a previous request to retrieve the next page of results.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of metrics to return in one page of results.
On success, responds with ListMetricAttributionMetricsOutput with field(s):
- metrics(Option<Vec<MetricAttribute>>):
 The metrics for the specified metric attribution.
- next_token(Option<String>):
 Specify the pagination token from a previous ListMetricAttributionMetricsResponse request to retrieve the next page of results.
On failure, responds with SdkError<ListMetricAttributionMetricsError>

source

pub fn list_metric_attributions(&self) -> ListMetricAttributions

Constructs a fluent builder for the ListMetricAttributions operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The metric attributions’ dataset group Amazon Resource Name (ARN).
- next_token(impl Into<String>) / set_next_token(Option<String>):
 Specify the pagination token from a previous request to retrieve the next page of results.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of metric attributions to return in one page of results.
On success, responds with ListMetricAttributionsOutput with field(s):
- metric_attributions(Option<Vec<MetricAttributionSummary>>):
 The list of metric attributions.
- next_token(Option<String>):
 Specify the pagination token from a previous request to retrieve the next page of results.
On failure, responds with SdkError<ListMetricAttributionsError>

source

pub fn list_recipes(&self) -> ListRecipes

Constructs a fluent builder for the ListRecipes operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- recipe_provider(RecipeProvider) / set_recipe_provider(Option<RecipeProvider>):
 The default is SERVICE.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListRecipes for getting the next set of recipes (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of recipes to return.
- domain(Domain) / set_domain(Option<Domain>):
 Filters returned recipes by domain for a Domain dataset group. Only recipes (Domain dataset group use cases) for this domain are included in the response. If you don’t specify a domain, all recipes are returned.
On success, responds with ListRecipesOutput with field(s):
- recipes(Option<Vec<RecipeSummary>>):
 The list of available recipes.
- next_token(Option<String>):
 A token for getting the next set of recipes.
On failure, responds with SdkError<ListRecipesError>

source

pub fn list_recommenders(&self) -> ListRecommenders

Constructs a fluent builder for the ListRecommenders operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the Domain dataset group to list the recommenders for. When a Domain dataset group is not specified, all the recommenders associated with the account are listed.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListRecommenders for getting the next set of recommenders (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of recommenders to return.
On success, responds with ListRecommendersOutput with field(s):
- recommenders(Option<Vec<RecommenderSummary>>):
 A list of the recommenders.
- next_token(Option<String>):
 A token for getting the next set of recommenders (if they exist).
On failure, responds with SdkError<ListRecommendersError>

source

pub fn list_schemas(&self) -> ListSchemas

Constructs a fluent builder for the ListSchemas operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListSchemas for getting the next set of schemas (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of schemas to return.
On success, responds with ListSchemasOutput with field(s):
- schemas(Option<Vec<DatasetSchemaSummary>>):
 A list of schemas.
- next_token(Option<String>):
 A token used to get the next set of schemas (if they exist).
On failure, responds with SdkError<ListSchemasError>

source

pub fn list_solutions(&self) -> ListSolutions

Constructs a fluent builder for the ListSolutions operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
 The Amazon Resource Name (ARN) of the dataset group.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListSolutions for getting the next set of solutions (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of solutions to return.
On success, responds with ListSolutionsOutput with field(s):
- solutions(Option<Vec<SolutionSummary>>):
 A list of the current solutions.
- next_token(Option<String>):
 A token for getting the next set of solutions (if they exist).
On failure, responds with SdkError<ListSolutionsError>

source

pub fn list_solution_versions(&self) -> ListSolutionVersions

Constructs a fluent builder for the ListSolutionVersions operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 A token returned from the previous call to ListSolutionVersions for getting the next set of solution versions (if they exist).
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of solution versions to return.
On success, responds with ListSolutionVersionsOutput with field(s):
- solution_versions(Option<Vec<SolutionVersionSummary>>):
 A list of solution versions describing the version properties.
- next_token(Option<String>):
 A token for getting the next set of solution versions (if they exist).
On failure, responds with SdkError<ListSolutionVersionsError>

source

pub fn list_tags_for_resource(&self) -> ListTagsForResource

Constructs a fluent builder for the ListTagsForResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The resource’s Amazon Resource Name.
On success, responds with ListTagsForResourceOutput with field(s):
- tags(Option<Vec<Tag>>):
 The resource’s tags.
On failure, responds with SdkError<ListTagsForResourceError>

source

pub fn start_recommender(&self) -> StartRecommender

Constructs a fluent builder for the StartRecommender operation.

The fluent builder is configurable:
- recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender to start.
On success, responds with StartRecommenderOutput with field(s):
- recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender you started.
On failure, responds with SdkError<StartRecommenderError>

source

pub fn stop_recommender(&self) -> StopRecommender

Constructs a fluent builder for the StopRecommender operation.

The fluent builder is configurable:
- recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender to stop.
On success, responds with StopRecommenderOutput with field(s):
- recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender you stopped.
On failure, responds with SdkError<StopRecommenderError>

source

pub fn stop_solution_version_creation(&self) -> StopSolutionVersionCreation

Constructs a fluent builder for the StopSolutionVersionCreation operation.

The fluent builder is configurable:
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The Amazon Resource Name (ARN) of the solution version you want to stop creating.
On success, responds with StopSolutionVersionCreationOutput
On failure, responds with SdkError<StopSolutionVersionCreationError>

source

pub fn tag_resource(&self) -> TagResource

Constructs a fluent builder for the TagResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The resource’s Amazon Resource Name (ARN).
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 Tags to apply to the resource. For more information see Tagging Personalize resources.
On success, responds with TagResourceOutput
On failure, responds with SdkError<TagResourceError>

source

pub fn untag_resource(&self) -> UntagResource

Constructs a fluent builder for the UntagResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The resource’s Amazon Resource Name (ARN).
- tag_keys(Vec<String>) / set_tag_keys(Option<Vec<String>>):
 Keys to remove from the resource’s tags.
On success, responds with UntagResourceOutput
On failure, responds with SdkError<UntagResourceError>

source

pub fn update_campaign(&self) -> UpdateCampaign

Constructs a fluent builder for the UpdateCampaign operation.

The fluent builder is configurable:
- campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
 The Amazon Resource Name (ARN) of the campaign.
- solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
 The ARN of a new solution version to deploy.
- min_provisioned_tps(i32) / set_min_provisioned_tps(Option<i32>):
 Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
- campaign_config(CampaignConfig) / set_campaign_config(Option<CampaignConfig>):
 The configuration details of a campaign.
On success, responds with UpdateCampaignOutput with field(s):
- campaign_arn(Option<String>):
 The same campaign ARN as given in the request.
On failure, responds with SdkError<UpdateCampaignError>

source

pub fn update_metric_attribution(&self) -> UpdateMetricAttribution

Constructs a fluent builder for the UpdateMetricAttribution operation.

The fluent builder is configurable:
- add_metrics(Vec<MetricAttribute>) / set_add_metrics(Option<Vec<MetricAttribute>>):
 Add new metric attributes to the metric attribution.
- remove_metrics(Vec<String>) / set_remove_metrics(Option<Vec<String>>):
 Remove metric attributes from the metric attribution.
- metrics_output_config(MetricAttributionOutput) / set_metrics_output_config(Option<MetricAttributionOutput>):
 An output config for the metric attribution.
- metric_attribution_arn(impl Into<String>) / set_metric_attribution_arn(Option<String>):
 The Amazon Resource Name (ARN) for the metric attribution to update.
On success, responds with UpdateMetricAttributionOutput with field(s):
- metric_attribution_arn(Option<String>):
 The Amazon Resource Name (ARN) for the metric attribution that you updated.
On failure, responds with SdkError<UpdateMetricAttributionError>

source

pub fn update_recommender(&self) -> UpdateRecommender

Constructs a fluent builder for the UpdateRecommender operation.

The fluent builder is configurable:
- recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
 The Amazon Resource Name (ARN) of the recommender to modify.
- recommender_config(RecommenderConfig) / set_recommender_config(Option<RecommenderConfig>):
 The configuration details of the recommender.
On success, responds with UpdateRecommenderOutput with field(s):
- recommender_arn(Option<String>):
 The same recommender Amazon Resource Name (ARN) as given in the request.
On failure, responds with SdkError<UpdateRecommenderError>

source §

impl Client

source

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.

source

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic if the conf is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
This method will panic if the conf is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.