Struct aws_sdk_personalize::client::Client

pub struct Client { /* private fields */ }

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.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_personalize::Client::new(&config);

Occasionally, SDKs may have additional service-specific values that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Config struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_personalize::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the CreateBatchInferenceJob operation has a Client::create_batch_inference_job, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.create_batch_inference_job()
    .job_name("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn create_batch_inference_job(&self) -> CreateBatchInferenceJobFluentBuilder

Constructs a fluent builder for the CreateBatchInferenceJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):
    required: true
    

    The name of the batch inference job to create.

    
    
  • solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
    required: true
    

    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>):
    required: false
    

    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>):
    required: false
    

    The number of recommendations to retrieve.

    
    
  • job_input(BatchInferenceJobInput) / set_job_input(Option<BatchInferenceJobInput>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: true
    

    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>):
    required: false
    

    The configuration details of a batch inference job.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    A list of tags to apply to the batch inference job.

    
    
  • batch_inference_job_mode(BatchInferenceJobMode) / set_batch_inference_job_mode(Option<BatchInferenceJobMode>):
    required: false
    

    The mode of the batch inference job. To generate descriptive themes for groups of similar items, set the job mode to THEME_GENERATION. If you don’t want to generate themes, use the default BATCH_INFERENCE.

    When you get batch recommendations with themes, you will incur additional costs. For more information, see Amazon Personalize pricing.

    
    
  • theme_generation_config(ThemeGenerationConfig) / set_theme_generation_config(Option<ThemeGenerationConfig>):
    required: false
    

    For theme generation jobs, specify the name of the column in your Items dataset that contains each item’s name.

    
    
• 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>

impl Client

pub fn create_batch_segment_job(&self) -> CreateBatchSegmentJobFluentBuilder

Constructs a fluent builder for the CreateBatchSegmentJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):
    required: true
    

    The name of the batch segment job to create.

    
    
  • solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
    required: true
    

    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>):
    required: false
    

    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>):
    required: false
    

    The number of predicted users generated by the batch segment job for each line of input data. The maximum number of users per segment is 5 million.

    
    
  • job_input(BatchSegmentJobInput) / set_job_input(Option<BatchSegmentJobInput>):
    required: true
    

    The Amazon S3 path for the input data used to generate the batch segment job.

    
    
  • job_output(BatchSegmentJobOutput) / set_job_output(Option<BatchSegmentJobOutput>):
    required: true
    

    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>):
    required: true
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_campaign(&self) -> CreateCampaignFluentBuilder

Constructs a fluent builder for the CreateCampaign operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    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>):
    required: true
    

    The Amazon Resource Name (ARN) of the trained model to deploy with the campaign. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.

    To deploy a model that isn’t the latest solution version of your solution, specify the ARN of the solution version.

    For more information about automatic campaign updates, see Enabling automatic campaign updates.

    
    
  • min_provisioned_tps(i32) / set_min_provisioned_tps(Option<i32>):
    required: false
    

    Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.

    
    
  • campaign_config(CampaignConfig) / set_campaign_config(Option<CampaignConfig>):
    required: false
    

    The configuration details of a campaign.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_dataset(&self) -> CreateDatasetFluentBuilder

Constructs a fluent builder for the CreateDataset operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name for the dataset.

    
    
  • schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
    required: true
    

    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>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset group to add the dataset to.

    
    
  • dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
    required: true
    

    The type of dataset.

    One of the following (case insensitive) values:

    • Interactions

    • Items

    • Users

    • Actions

    • Action_Interactions

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_dataset_export_job(&self) -> CreateDatasetExportJobFluentBuilder

Constructs a fluent builder for the CreateDatasetExportJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):
    required: true
    

    The name for the dataset export job.

    
    
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset that contains the data to export.

    
    
  • ingestion_mode(IngestionMode) / set_ingestion_mode(Option<IngestionMode>):
    required: false
    

    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>):
    required: true
    

    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>):
    required: true
    

    The path to the Amazon S3 bucket where the job’s output is stored.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_dataset_group(&self) -> CreateDatasetGroupFluentBuilder

Constructs a fluent builder for the CreateDatasetGroup operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name for the new dataset group.

    
    
  • role_arn(impl Into<String>) / set_role_arn(Option<String>):
    required: false
    

    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>):
    required: false
    

    The Amazon Resource Name (ARN) of a Key Management Service (KMS) key used to encrypt the datasets.

    
    
  • domain(Domain) / set_domain(Option<Domain>):
    required: false
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_dataset_import_job(&self) -> CreateDatasetImportJobFluentBuilder

Constructs a fluent builder for the CreateDatasetImportJob operation.

• The fluent builder is configurable:
  • job_name(impl Into<String>) / set_job_name(Option<String>):
    required: true
    

    The name for the dataset import job.

    
    
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The ARN of the dataset that receives the imported data.

    
    
  • data_source(DataSource) / set_data_source(Option<DataSource>):
    required: true
    

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

    
    
  • role_arn(impl Into<String>) / set_role_arn(Option<String>):
    required: true
    

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

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

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

    
    
  • import_mode(ImportMode) / set_import_mode(Option<ImportMode>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn create_event_tracker(&self) -> CreateEventTrackerFluentBuilder

Constructs a fluent builder for the CreateEventTracker operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name for the event tracker.

    
    
  • dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset group that receives the event data.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_filter(&self) -> CreateFilterFluentBuilder

Constructs a fluent builder for the CreateFilter operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the filter to create.

    
    
  • dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
    required: true
    

    The ARN of the dataset group that the filter will belong to.

    
    
  • filter_expression(impl Into<String>) / set_filter_expression(Option<String>):
    required: true
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_metric_attribution(&self) -> CreateMetricAttributionFluentBuilder

Constructs a fluent builder for the CreateMetricAttribution operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    A name for the metric attribution.

    
    
  • dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the destination dataset group for the metric attribution.

    
    
  • metrics(MetricAttribute) / set_metrics(Option<Vec::<MetricAttribute>>):
    required: true
    

    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>):
    required: true
    

    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>

impl Client

pub fn create_recommender(&self) -> CreateRecommenderFluentBuilder

Constructs a fluent builder for the CreateRecommender operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name of the recommender.

    
    
  • dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the destination domain dataset group for the recommender.

    
    
  • recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
    required: true
    

    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>):
    required: false
    

    The configuration details of the recommender.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_schema(&self) -> CreateSchemaFluentBuilder

Constructs a fluent builder for the CreateSchema operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name for the schema.

    
    
  • schema(impl Into<String>) / set_schema(Option<String>):
    required: true
    

    A schema in Avro JSON format.

    
    
  • domain(Domain) / set_domain(Option<Domain>):
    required: false
    

    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>

impl Client

pub fn create_solution(&self) -> CreateSolutionFluentBuilder

Constructs a fluent builder for the CreateSolution operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: true
    

    The name for the solution.

    
    
  • perform_hpo(bool) / set_perform_hpo(Option<bool>):
    required: false
    

    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(Option<bool>):
    required: false
    

    We don’t recommend enabling automated machine learning. Instead, match your use case to the available Amazon Personalize recipes. For more information, see Choosing a recipe.

    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.

    
    
  • perform_auto_training(bool) / set_perform_auto_training(Option<bool>):
    required: false
    

    Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.

    Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.

    After training starts, you can get the solution version’s Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.

    
    
  • recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.

    
    
  • dataset_group_arn(impl Into<String>) / set_dataset_group_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset group that provides the training data.

    
    
  • event_type(impl Into<String>) / set_event_type(Option<String>):
    required: false
    

    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>):
    required: false
    

    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(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn create_solution_version(&self) -> CreateSolutionVersionFluentBuilder

Constructs a fluent builder for the CreateSolutionVersion operation.

• The fluent builder is configurable:
  • name(impl Into<String>) / set_name(Option<String>):
    required: false
    

    The name of the solution version.

    
    
  • solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the solution containing the training configuration information.

    
    
  • training_mode(TrainingMode) / set_training_mode(Option<TrainingMode>):
    required: false
    

    The scope of training to be performed when creating the solution version. The default is FULL. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.

    If you use User-Personalization, you can specify a training mode of UPDATE. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify UPDATE, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to FULL and deploy it in a campaign. For more information about automatic updates, see Automatic updates.

    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 legacy HRNN-Coldstart recipe.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    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>

impl Client

pub fn delete_campaign(&self) -> DeleteCampaignFluentBuilder

Constructs a fluent builder for the DeleteCampaign operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the campaign to delete.

    
    
• On success, responds with DeleteCampaignOutput
• On failure, responds with SdkError<DeleteCampaignError>

impl Client

pub fn delete_dataset(&self) -> DeleteDatasetFluentBuilder

Constructs a fluent builder for the DeleteDataset operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset to delete.

    
    
• On success, responds with DeleteDatasetOutput
• On failure, responds with SdkError<DeleteDatasetError>

impl Client

pub fn delete_dataset_group(&self) -> DeleteDatasetGroupFluentBuilder

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>):
    required: true
    

    The ARN of the dataset group to delete.

    
    
• On success, responds with DeleteDatasetGroupOutput
• On failure, responds with SdkError<DeleteDatasetGroupError>

impl Client

pub fn delete_event_tracker(&self) -> DeleteEventTrackerFluentBuilder

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>):
    required: true
    

    The Amazon Resource Name (ARN) of the event tracker to delete.

    
    
• On success, responds with DeleteEventTrackerOutput
• On failure, responds with SdkError<DeleteEventTrackerError>

impl Client

pub fn delete_filter(&self) -> DeleteFilterFluentBuilder

Constructs a fluent builder for the DeleteFilter operation.

• The fluent builder is configurable:
  • filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
    required: true
    

    The ARN of the filter to delete.

    
    
• On success, responds with DeleteFilterOutput
• On failure, responds with SdkError<DeleteFilterError>

impl Client

pub fn delete_metric_attribution(&self) -> DeleteMetricAttributionFluentBuilder

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>):
    required: true
    

    The metric attribution’s Amazon Resource Name (ARN).

    
    
• On success, responds with DeleteMetricAttributionOutput
• On failure, responds with SdkError<DeleteMetricAttributionError>

impl Client

pub fn delete_recommender(&self) -> DeleteRecommenderFluentBuilder

Constructs a fluent builder for the DeleteRecommender operation.

• The fluent builder is configurable:
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the recommender to delete.

    
    
• On success, responds with DeleteRecommenderOutput
• On failure, responds with SdkError<DeleteRecommenderError>

impl Client

pub fn delete_schema(&self) -> DeleteSchemaFluentBuilder

Constructs a fluent builder for the DeleteSchema operation.

• The fluent builder is configurable:
  • schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the schema to delete.

    
    
• On success, responds with DeleteSchemaOutput
• On failure, responds with SdkError<DeleteSchemaError>

impl Client

pub fn delete_solution(&self) -> DeleteSolutionFluentBuilder

Constructs a fluent builder for the DeleteSolution operation.

• The fluent builder is configurable:
  • solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
    required: true
    

    The ARN of the solution to delete.

    
    
• On success, responds with DeleteSolutionOutput
• On failure, responds with SdkError<DeleteSolutionError>

impl Client

pub fn describe_algorithm(&self) -> DescribeAlgorithmFluentBuilder

Constructs a fluent builder for the DescribeAlgorithm operation.

• The fluent builder is configurable:
  • algorithm_arn(impl Into<String>) / set_algorithm_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_batch_inference_job( &self ) -> DescribeBatchInferenceJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_batch_segment_job(&self) -> DescribeBatchSegmentJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_campaign(&self) -> DescribeCampaignFluentBuilder

Constructs a fluent builder for the DescribeCampaign operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_dataset(&self) -> DescribeDatasetFluentBuilder

Constructs a fluent builder for the DescribeDataset operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_dataset_export_job( &self ) -> DescribeDatasetExportJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_dataset_group(&self) -> DescribeDatasetGroupFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_dataset_import_job( &self ) -> DescribeDatasetImportJobFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_event_tracker(&self) -> DescribeEventTrackerFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_feature_transformation( &self ) -> DescribeFeatureTransformationFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_filter(&self) -> DescribeFilterFluentBuilder

Constructs a fluent builder for the DescribeFilter operation.

• The fluent builder is configurable:
  • filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_metric_attribution( &self ) -> DescribeMetricAttributionFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn describe_recipe(&self) -> DescribeRecipeFluentBuilder

Constructs a fluent builder for the DescribeRecipe operation.

• The fluent builder is configurable:
  • recipe_arn(impl Into<String>) / set_recipe_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_recommender(&self) -> DescribeRecommenderFluentBuilder

Constructs a fluent builder for the DescribeRecommender operation.

• The fluent builder is configurable:
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_schema(&self) -> DescribeSchemaFluentBuilder

Constructs a fluent builder for the DescribeSchema operation.

• The fluent builder is configurable:
  • schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_solution(&self) -> DescribeSolutionFluentBuilder

Constructs a fluent builder for the DescribeSolution operation.

• The fluent builder is configurable:
  • solution_arn(impl Into<String>) / set_solution_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn describe_solution_version(&self) -> DescribeSolutionVersionFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn get_solution_metrics(&self) -> GetSolutionMetricsFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn list_batch_inference_jobs(&self) -> ListBatchInferenceJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The token to request the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    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>

impl Client

pub fn list_batch_segment_jobs(&self) -> ListBatchSegmentJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    The token to request the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    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>

impl Client

pub fn list_campaigns(&self) -> ListCampaignsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_dataset_export_jobs(&self) -> ListDatasetExportJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_dataset_groups(&self) -> ListDatasetGroupsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_dataset_import_jobs(&self) -> ListDatasetImportJobsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_datasets(&self) -> ListDatasetsFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    A token returned from the previous call to ListDatasets for getting the next set of dataset import jobs (if they exist).

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    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>

impl Client

pub fn list_event_trackers(&self) -> ListEventTrackersFluentBuilder

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>):
    required: false
    

    The ARN of a dataset group used to filter the response.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_filters(&self) -> ListFiltersFluentBuilder

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>):
    required: false
    

    The ARN of the dataset group that contains the filters.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_metric_attribution_metrics( &self ) -> ListMetricAttributionMetricsFluentBuilder

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>):
    required: false
    

    The Amazon Resource Name (ARN) of the metric attribution to retrieve attributes for.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Specify the pagination token from a previous request to retrieve the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    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>

impl Client

pub fn list_metric_attributions(&self) -> ListMetricAttributionsFluentBuilder

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>):
    required: false
    

    The metric attributions’ dataset group Amazon Resource Name (ARN).

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Specify the pagination token from a previous request to retrieve the next page of results.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    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>

impl Client

pub fn list_recipes(&self) -> ListRecipesFluentBuilder

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>):
    required: false
    

    The default is SERVICE.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    The maximum number of recipes to return.

    
    
  • domain(Domain) / set_domain(Option<Domain>):
    required: false
    

    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>

impl Client

pub fn list_recommenders(&self) -> ListRecommendersFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_schemas(&self) -> ListSchemasFluentBuilder

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>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_solution_versions(&self) -> ListSolutionVersionsFluentBuilder

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>):
    required: false
    

    The Amazon Resource Name (ARN) of the solution.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_solutions(&self) -> ListSolutionsFluentBuilder

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>):
    required: false
    

    The Amazon Resource Name (ARN) of the dataset group.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    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>):
    required: false
    

    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>

impl Client

pub fn list_tags_for_resource(&self) -> ListTagsForResourceFluentBuilder

Constructs a fluent builder for the ListTagsForResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The resource’s Amazon Resource Name (ARN).

    
    
• On success, responds with ListTagsForResourceOutput with field(s):
  • tags(Option<Vec::<Tag>>):

    The resource’s tags.

• On failure, responds with SdkError<ListTagsForResourceError>

impl Client

pub fn start_recommender(&self) -> StartRecommenderFluentBuilder

Constructs a fluent builder for the StartRecommender operation.

• The fluent builder is configurable:
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn stop_recommender(&self) -> StopRecommenderFluentBuilder

Constructs a fluent builder for the StopRecommender operation.

• The fluent builder is configurable:
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: true
    

    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>

impl Client

pub fn stop_solution_version_creation( &self ) -> StopSolutionVersionCreationFluentBuilder

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>):
    required: true
    

    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>

impl Client

pub fn tag_resource(&self) -> TagResourceFluentBuilder

Constructs a fluent builder for the TagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The resource’s Amazon Resource Name (ARN).

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: true
    

    Tags to apply to the resource. For more information see Tagging Amazon Personalize resources.

    
    
• On success, responds with TagResourceOutput
• On failure, responds with SdkError<TagResourceError>

impl Client

pub fn untag_resource(&self) -> UntagResourceFluentBuilder

Constructs a fluent builder for the UntagResource operation.

• The fluent builder is configurable:
  • resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
    required: true
    

    The resource’s Amazon Resource Name (ARN).

    
    
  • tag_keys(impl Into<String>) / set_tag_keys(Option<Vec::<String>>):
    required: true
    

    The keys of the tags to be removed.

    
    
• On success, responds with UntagResourceOutput
• On failure, responds with SdkError<UntagResourceError>

impl Client

pub fn update_campaign(&self) -> UpdateCampaignFluentBuilder

Constructs a fluent builder for the UpdateCampaign operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the campaign.

    
    
  • solution_version_arn(impl Into<String>) / set_solution_version_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of a new model to deploy. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.

    To deploy a model that isn’t the latest solution version of your solution, specify the ARN of the solution version.

    For more information about automatic campaign updates, see Enabling automatic campaign updates.

    
    
  • min_provisioned_tps(i32) / set_min_provisioned_tps(Option<i32>):
    required: false
    

    Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.

    
    
  • campaign_config(CampaignConfig) / set_campaign_config(Option<CampaignConfig>):
    required: false
    

    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>

impl Client

pub fn update_dataset(&self) -> UpdateDatasetFluentBuilder

Constructs a fluent builder for the UpdateDataset operation.

• The fluent builder is configurable:
  • dataset_arn(impl Into<String>) / set_dataset_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the dataset that you want to update.

    
    
  • schema_arn(impl Into<String>) / set_schema_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the new schema you want use.

    
    
• On success, responds with UpdateDatasetOutput with field(s):
  • dataset_arn(Option<String>):

    The Amazon Resource Name (ARN) of the dataset you updated.

• On failure, responds with SdkError<UpdateDatasetError>

impl Client

pub fn update_metric_attribution(&self) -> UpdateMetricAttributionFluentBuilder

Constructs a fluent builder for the UpdateMetricAttribution operation.

• The fluent builder is configurable:
  • add_metrics(MetricAttribute) / set_add_metrics(Option<Vec::<MetricAttribute>>):
    required: false
    

    Add new metric attributes to the metric attribution.

    
    
  • remove_metrics(impl Into<String>) / set_remove_metrics(Option<Vec::<String>>):
    required: false
    

    Remove metric attributes from the metric attribution.

    
    
  • metrics_output_config(MetricAttributionOutput) / set_metrics_output_config(Option<MetricAttributionOutput>):
    required: false
    

    An output config for the metric attribution.

    
    
  • metric_attribution_arn(impl Into<String>) / set_metric_attribution_arn(Option<String>):
    required: false
    

    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>

impl Client

pub fn update_recommender(&self) -> UpdateRecommenderFluentBuilder

Constructs a fluent builder for the UpdateRecommender operation.

• The fluent builder is configurable:
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the recommender to modify.

    
    
  • recommender_config(RecommenderConfig) / set_recommender_config(Option<RecommenderConfig>):
    required: true
    

    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>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

• Retries or timeouts are enabled without a sleep_impl configured.
• Identity caching is enabled without a sleep_impl and time_source configured.
• No behavior_version is provided.

The panic message for each of these will have instructions on how to resolve them.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

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.
• This method will panic if no BehaviorVersion is provided. If you experience this panic, set behavior_version on the Config or enable the behavior-version-latest Cargo feature.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

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

Formats the value using the given formatter.