Struct aws_sdk_neptunedata::client::Client

pub struct Client { /* private fields */ }

Client for Amazon NeptuneData

Client for invoking operations on Amazon NeptuneData. Each operation on Amazon NeptuneData 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_neptunedata::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_neptunedata::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 CancelGremlinQuery operation has a Client::cancel_gremlin_query, 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.cancel_gremlin_query()
    .query_id("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 cancel_gremlin_query(&self) -> CancelGremlinQueryFluentBuilder

Constructs a fluent builder for the CancelGremlinQuery operation.

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

    The unique identifier that identifies the query to be canceled.

    
    
• On success, responds with CancelGremlinQueryOutput with field(s):
  • status(Option<String>):

    The status of the cancelation

• On failure, responds with SdkError<CancelGremlinQueryError>

impl Client

pub fn cancel_loader_job(&self) -> CancelLoaderJobFluentBuilder

Constructs a fluent builder for the CancelLoaderJob operation.

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

    The ID of the load job to be deleted.

    
    
• On success, responds with CancelLoaderJobOutput with field(s):
  • status(Option<String>):

    The cancellation status.

• On failure, responds with SdkError<CancelLoaderJobError>

impl Client

pub fn cancel_ml_data_processing_job( &self ) -> CancelMLDataProcessingJobFluentBuilder

Constructs a fluent builder for the CancelMLDataProcessingJob operation.

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

    The unique identifier of the data-processing job.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • clean(bool) / set_clean(Option<bool>):
    required: false
    

    If set to TRUE, this flag specifies that all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE.

    
    
• On success, responds with CancelMlDataProcessingJobOutput with field(s):
  • status(Option<String>):

    The status of the cancellation request.

• On failure, responds with SdkError<CancelMLDataProcessingJobError>

impl Client

pub fn cancel_ml_model_training_job( &self ) -> CancelMLModelTrainingJobFluentBuilder

Constructs a fluent builder for the CancelMLModelTrainingJob operation.

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

    The unique identifier of the model-training job to be canceled.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • clean(bool) / set_clean(Option<bool>):
    required: false
    

    If set to TRUE, this flag specifies that all Amazon S3 artifacts should be deleted when the job is stopped. The default is FALSE.

    
    
• On success, responds with CancelMlModelTrainingJobOutput with field(s):
  • status(Option<String>):

    The status of the cancellation.

• On failure, responds with SdkError<CancelMLModelTrainingJobError>

impl Client

pub fn cancel_ml_model_transform_job( &self ) -> CancelMLModelTransformJobFluentBuilder

Constructs a fluent builder for the CancelMLModelTransformJob operation.

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

    The unique ID of the model transform job to be canceled.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • clean(bool) / set_clean(Option<bool>):
    required: false
    

    If this flag is set to TRUE, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE.

    
    
• On success, responds with CancelMlModelTransformJobOutput with field(s):
  • status(Option<String>):

    the status of the cancelation.

• On failure, responds with SdkError<CancelMLModelTransformJobError>

impl Client

pub fn cancel_open_cypher_query(&self) -> CancelOpenCypherQueryFluentBuilder

Constructs a fluent builder for the CancelOpenCypherQuery operation.

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

    The unique ID of the openCypher query to cancel.

    
    
  • silent(bool) / set_silent(Option<bool>):
    required: false
    

    If set to TRUE, causes the cancelation of the openCypher query to happen silently.

    
    
• On success, responds with CancelOpenCypherQueryOutput with field(s):
  • status(Option<String>):

    The cancellation status of the openCypher query.

  • payload(Option<bool>):

    The cancelation payload for the openCypher query.

• On failure, responds with SdkError<CancelOpenCypherQueryError>

impl Client

pub fn create_ml_endpoint(&self) -> CreateMLEndpointFluentBuilder

Constructs a fluent builder for the CreateMLEndpoint operation.

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

    A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

    
    
  • ml_model_training_job_id(impl Into<String>) / set_ml_model_training_job_id(Option<String>):
    required: false
    

    The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

    
    
  • ml_model_transform_job_id(impl Into<String>) / set_ml_model_transform_job_id(Option<String>):
    required: false
    

    The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

    
    
  • update(bool) / set_update(Option<bool>):
    required: false
    

    If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

    
    
  • model_name(impl Into<String>) / set_model_name(Option<String>):
    required: false
    

    Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn. Valid values for knowledge graphs are: kge, transe, distmult, and rotate.

    
    
  • instance_type(impl Into<String>) / set_instance_type(Option<String>):
    required: false
    

    The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

    
    
  • instance_count(i32) / set_instance_count(Option<i32>):
    required: false
    

    The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

    
    
  • volume_encryption_kms_key(impl Into<String>) / set_volume_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

    
    
• On success, responds with CreateMlEndpointOutput with field(s):
  • id(Option<String>):

    The unique ID of the new inference endpoint.

  • arn(Option<String>):

    The ARN for the new inference endpoint.

  • creation_time_in_millis(Option<i64>):

    The endpoint creation time, in milliseconds.

• On failure, responds with SdkError<CreateMLEndpointError>

impl Client

pub fn delete_ml_endpoint(&self) -> DeleteMLEndpointFluentBuilder

Constructs a fluent builder for the DeleteMLEndpoint operation.

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

    The unique identifier of the inference endpoint.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

    
    
  • clean(bool) / set_clean(Option<bool>):
    required: false
    

    If this flag is set to TRUE, all Neptune ML S3 artifacts should be deleted when the job is stopped. The default is FALSE.

    
    
• On success, responds with DeleteMlEndpointOutput with field(s):
  • status(Option<String>):

    The status of the cancellation.

• On failure, responds with SdkError<DeleteMLEndpointError>

impl Client

pub fn delete_propertygraph_statistics( &self ) -> DeletePropertygraphStatisticsFluentBuilder

Constructs a fluent builder for the DeletePropertygraphStatistics operation.

• The fluent builder takes no input, just send it.
• On success, responds with DeletePropertygraphStatisticsOutput with field(s):
  • status_code(Option<i32>):

    The HTTP response code: 200 if the delete was successful, or 204 if there were no statistics to delete.

  • status(Option<String>):

    The cancel status.

  • payload(Option<DeleteStatisticsValueMap>):

    The deletion payload.

• On failure, responds with SdkError<DeletePropertygraphStatisticsError>

impl Client

pub fn delete_sparql_statistics(&self) -> DeleteSparqlStatisticsFluentBuilder

Constructs a fluent builder for the DeleteSparqlStatistics operation.

• The fluent builder takes no input, just send it.
• On success, responds with DeleteSparqlStatisticsOutput with field(s):
  • status_code(Option<i32>):

    The HTTP response code: 200 if the delete was successful, or 204 if there were no statistics to delete.

  • status(Option<String>):

    The cancel status.

  • payload(Option<DeleteStatisticsValueMap>):

    The deletion payload.

• On failure, responds with SdkError<DeleteSparqlStatisticsError>

impl Client

pub fn execute_fast_reset(&self) -> ExecuteFastResetFluentBuilder

Constructs a fluent builder for the ExecuteFastReset operation.

• The fluent builder is configurable:
  • action(Action) / set_action(Option<Action>):
    required: true
    

    The fast reset action. One of the following values:

    • initiateDatabaseReset   –   This action generates a unique token needed to actually perform the fast reset.

    • performDatabaseReset   –   This action uses the token generated by the initiateDatabaseReset action to actually perform the fast reset.

    
    
  • token(impl Into<String>) / set_token(Option<String>):
    required: false
    

    The fast-reset token to initiate the reset.

    
    
• On success, responds with ExecuteFastResetOutput with field(s):
  • status(String):

    The status is only returned for the performDatabaseReset action, and indicates whether or not the fast reset rquest is accepted.

  • payload(Option<FastResetToken>):

    The payload is only returned by the initiateDatabaseReset action, and contains the unique token to use with the performDatabaseReset action to make the reset occur.

• On failure, responds with SdkError<ExecuteFastResetError>

impl Client

pub fn execute_gremlin_explain_query( &self ) -> ExecuteGremlinExplainQueryFluentBuilder

Constructs a fluent builder for the ExecuteGremlinExplainQuery operation.

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

    The Gremlin explain query string.

    
    
• On success, responds with ExecuteGremlinExplainQueryOutput with field(s):
  • output(Option<Blob>):

    A text blob containing the Gremlin explain result, as described in Tuning Gremlin queries.

• On failure, responds with SdkError<ExecuteGremlinExplainQueryError>

impl Client

pub fn execute_gremlin_profile_query( &self ) -> ExecuteGremlinProfileQueryFluentBuilder

Constructs a fluent builder for the ExecuteGremlinProfileQuery operation.

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

    The Gremlin query string to profile.

    
    
  • results(bool) / set_results(Option<bool>):
    required: false
    

    If this flag is set to TRUE, the query results are gathered and displayed as part of the profile report. If FALSE, only the result count is displayed.

    
    
  • chop(i32) / set_chop(Option<i32>):
    required: false
    

    If non-zero, causes the results string to be truncated at that number of characters. If set to zero, the string contains all the results.

    
    
  • serializer(impl Into<String>) / set_serializer(Option<String>):
    required: false
    

    If non-null, the gathered results are returned in a serialized response message in the format specified by this parameter. See Gremlin profile API in Neptune for more information.

    
    
  • index_ops(bool) / set_index_ops(Option<bool>):
    required: false
    

    If this flag is set to TRUE, the results include a detailed report of all index operations that took place during query execution and serialization.

    
    
• On success, responds with ExecuteGremlinProfileQueryOutput with field(s):
  • output(Option<Blob>):

    A text blob containing the Gremlin Profile result. See Gremlin profile API in Neptune for details.

• On failure, responds with SdkError<ExecuteGremlinProfileQueryError>

impl Client

pub fn execute_gremlin_query(&self) -> ExecuteGremlinQueryFluentBuilder

Constructs a fluent builder for the ExecuteGremlinQuery operation.

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

    Using this API, you can run Gremlin queries in string format much as you can using the HTTP endpoint. The interface is compatible with whatever Gremlin version your DB cluster is using (see the Tinkerpop client section to determine which Gremlin releases your engine version supports).

    
    
  • serializer(impl Into<String>) / set_serializer(Option<String>):
    required: false
    

    If non-null, the query results are returned in a serialized response message in the format specified by this parameter. See the GraphSON section in the TinkerPop documentation for a list of the formats that are currently supported.

    
    
• On success, responds with ExecuteGremlinQueryOutput with field(s):
  • request_id(Option<String>):

    The unique identifier of the Gremlin query.

  • status(Option<GremlinQueryStatusAttributes>):

    The status of the Gremlin query.

  • result(Option<Document>):

    The Gremlin query output from the server.

  • meta_value(Option<Document>):

    Metadata about the Gremlin query.

• On failure, responds with SdkError<ExecuteGremlinQueryError>

impl Client

pub fn execute_open_cypher_explain_query( &self ) -> ExecuteOpenCypherExplainQueryFluentBuilder

Constructs a fluent builder for the ExecuteOpenCypherExplainQuery operation.

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

    The openCypher query string.

    
    
  • parameters(impl Into<String>) / set_parameters(Option<String>):
    required: false
    

    The openCypher query parameters.

    
    
  • explain_mode(OpenCypherExplainMode) / set_explain_mode(Option<OpenCypherExplainMode>):
    required: true
    

    The openCypher explain mode. Can be one of: static, dynamic, or details.

    
    
• On success, responds with ExecuteOpenCypherExplainQueryOutput with field(s):
  • results(Blob):

    A text blob containing the openCypher explain results.

• On failure, responds with SdkError<ExecuteOpenCypherExplainQueryError>

impl Client

pub fn execute_open_cypher_query(&self) -> ExecuteOpenCypherQueryFluentBuilder

Constructs a fluent builder for the ExecuteOpenCypherQuery operation.

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

    The openCypher query string to be executed.

    
    
  • parameters(impl Into<String>) / set_parameters(Option<String>):
    required: false
    

    The openCypher query parameters for query execution. See Examples of openCypher parameterized queries for more information.

    
    
• On success, responds with ExecuteOpenCypherQueryOutput with field(s):
  • results(Document):

    The openCypherquery results.

• On failure, responds with SdkError<ExecuteOpenCypherQueryError>

impl Client

pub fn get_engine_status(&self) -> GetEngineStatusFluentBuilder

Constructs a fluent builder for the GetEngineStatus operation.

• The fluent builder takes no input, just send it.
• On success, responds with GetEngineStatusOutput with field(s):
  • status(Option<String>):

    Set to healthy if the instance is not experiencing problems. If the instance is recovering from a crash or from being rebooted and there are active transactions running from the latest server shutdown, status is set to recovery.

  • start_time(Option<String>):

    Set to the UTC time at which the current server process started.

  • db_engine_version(Option<String>):

    Set to the Neptune engine version running on your DB cluster. If this engine version has been manually patched since it was released, the version number is prefixed by Patch-.

  • role(Option<String>):

    Set to reader if the instance is a read-replica, or to writer if the instance is the primary instance.

  • dfe_query_engine(Option<String>):

    Set to enabled if the DFE engine is fully enabled, or to viaQueryHint (the default) if the DFE engine is only used with queries that have the useDFE query hint set to true.

  • gremlin(Option<QueryLanguageVersion>):

    Contains information about the Gremlin query language available on your cluster. Specifically, it contains a version field that specifies the current TinkerPop version being used by the engine.

  • sparql(Option<QueryLanguageVersion>):

    Contains information about the SPARQL query language available on your cluster. Specifically, it contains a version field that specifies the current SPARQL version being used by the engine.

  • opencypher(Option<QueryLanguageVersion>):

    Contains information about the openCypher query language available on your cluster. Specifically, it contains a version field that specifies the current operCypher version being used by the engine.

  • lab_mode(Option<HashMap::<String, String>>):

    Contains Lab Mode settings being used by the engine.

  • rolling_back_trx_count(Option<i32>):

    If there are transactions being rolled back, this field is set to the number of such transactions. If there are none, the field doesn’t appear at all.

  • rolling_back_trx_earliest_start_time(Option<String>):

    Set to the start time of the earliest transaction being rolled back. If no transactions are being rolled back, the field doesn’t appear at all.

  • features(Option<HashMap::<String, Document>>):

    Contains status information about the features enabled on your DB cluster.

  • settings(Option<HashMap::<String, String>>):

    Contains information about the current settings on your DB cluster. For example, contains the current cluster query timeout setting (clusterQueryTimeoutInMs).

• On failure, responds with SdkError<GetEngineStatusError>

impl Client

pub fn get_gremlin_query_status(&self) -> GetGremlinQueryStatusFluentBuilder

Constructs a fluent builder for the GetGremlinQueryStatus operation.

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

    The unique identifier that identifies the Gremlin query.

    
    
• On success, responds with GetGremlinQueryStatusOutput with field(s):
  • query_id(Option<String>):

    The ID of the query for which status is being returned.

  • query_string(Option<String>):

    The Gremlin query string.

  • query_eval_stats(Option<QueryEvalStats>):

    The evaluation status of the Gremlin query.

• On failure, responds with SdkError<GetGremlinQueryStatusError>

impl Client

pub fn get_loader_job_status(&self) -> GetLoaderJobStatusFluentBuilder

Constructs a fluent builder for the GetLoaderJobStatus operation.

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

    The load ID of the load job to get the status of.

    
    
  • details(bool) / set_details(Option<bool>):
    required: false
    

    Flag indicating whether or not to include details beyond the overall status (TRUE or FALSE; the default is FALSE).

    
    
  • errors(bool) / set_errors(Option<bool>):
    required: false
    

    Flag indicating whether or not to include a list of errors encountered (TRUE or FALSE; the default is FALSE).

    The list of errors is paged. The page and errorsPerPage parameters allow you to page through all the errors.

    
    
  • page(i32) / set_page(Option<i32>):
    required: false
    

    The error page number (a positive integer; the default is 1). Only valid when the errors parameter is set to TRUE.

    
    
  • errors_per_page(i32) / set_errors_per_page(Option<i32>):
    required: false
    

    The number of errors returned in each page (a positive integer; the default is 10). Only valid when the errors parameter set to TRUE.

    
    
• On success, responds with GetLoaderJobStatusOutput with field(s):
  • status(String):

    The HTTP response code for the request.

  • payload(Document):

    Status information about the load job, in a layout that could look like this:

• On failure, responds with SdkError<GetLoaderJobStatusError>

impl Client

pub fn get_ml_data_processing_job(&self) -> GetMLDataProcessingJobFluentBuilder

Constructs a fluent builder for the GetMLDataProcessingJob operation.

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

    The unique identifier of the data-processing job to be retrieved.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with GetMlDataProcessingJobOutput with field(s):
  • status(Option<String>):

    Status of the data processing job.

  • id(Option<String>):

    The unique identifier of this data-processing job.

  • processing_job(Option<MlResourceDefinition>):

    Definition of the data processing job.

• On failure, responds with SdkError<GetMLDataProcessingJobError>

impl Client

pub fn get_ml_endpoint(&self) -> GetMLEndpointFluentBuilder

Constructs a fluent builder for the GetMLEndpoint operation.

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

    The unique identifier of the inference endpoint.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with GetMlEndpointOutput with field(s):
  • status(Option<String>):

    The status of the inference endpoint.

  • id(Option<String>):

    The unique identifier of the inference endpoint.

  • endpoint(Option<MlResourceDefinition>):

    The endpoint definition.

  • endpoint_config(Option<MlConfigDefinition>):

    The endpoint configuration

• On failure, responds with SdkError<GetMLEndpointError>

impl Client

pub fn get_ml_model_training_job(&self) -> GetMLModelTrainingJobFluentBuilder

Constructs a fluent builder for the GetMLModelTrainingJob operation.

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

    The unique identifier of the model-training job to retrieve.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with GetMlModelTrainingJobOutput with field(s):
  • status(Option<String>):

    The status of the model training job.

  • id(Option<String>):

    The unique identifier of this model-training job.

  • processing_job(Option<MlResourceDefinition>):

    The data processing job.

  • hpo_job(Option<MlResourceDefinition>):

    The HPO job.

  • model_transform_job(Option<MlResourceDefinition>):

    The model transform job.

  • ml_models(Option<Vec::<MlConfigDefinition>>):

    A list of the configurations of the ML models being used.

• On failure, responds with SdkError<GetMLModelTrainingJobError>

impl Client

pub fn get_ml_model_transform_job(&self) -> GetMLModelTransformJobFluentBuilder

Constructs a fluent builder for the GetMLModelTransformJob operation.

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

    The unique identifier of the model-transform job to be reetrieved.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with GetMlModelTransformJobOutput with field(s):
  • status(Option<String>):

    The status of the model-transform job.

  • id(Option<String>):

    The unique identifier of the model-transform job to be retrieved.

  • base_processing_job(Option<MlResourceDefinition>):

    The base data processing job.

  • remote_model_transform_job(Option<MlResourceDefinition>):

    The remote model transform job.

  • models(Option<Vec::<MlConfigDefinition>>):

    A list of the configuration information for the models being used.

• On failure, responds with SdkError<GetMLModelTransformJobError>

impl Client

pub fn get_open_cypher_query_status( &self ) -> GetOpenCypherQueryStatusFluentBuilder

Constructs a fluent builder for the GetOpenCypherQueryStatus operation.

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

    The unique ID of the openCypher query for which to retrieve the query status.

    
    
• On success, responds with GetOpenCypherQueryStatusOutput with field(s):
  • query_id(Option<String>):

    The unique ID of the query for which status is being returned.

  • query_string(Option<String>):

    The openCypher query string.

  • query_eval_stats(Option<QueryEvalStats>):

    The openCypher query evaluation status.

• On failure, responds with SdkError<GetOpenCypherQueryStatusError>

impl Client

pub fn get_propertygraph_statistics( &self ) -> GetPropertygraphStatisticsFluentBuilder

Constructs a fluent builder for the GetPropertygraphStatistics operation.

• The fluent builder takes no input, just send it.
• On success, responds with GetPropertygraphStatisticsOutput with field(s):
  • status(String):

    The HTTP return code of the request. If the request succeeded, the code is 200. See Common error codes for DFE statistics request for a list of common errors.

  • payload(Option<Statistics>):

    Statistics for property-graph data.

• On failure, responds with SdkError<GetPropertygraphStatisticsError>

impl Client

pub fn get_propertygraph_stream(&self) -> GetPropertygraphStreamFluentBuilder

Constructs a fluent builder for the GetPropertygraphStream operation.

• The fluent builder is configurable:
  • limit(i64) / set_limit(Option<i64>):
    required: false
    

    Specifies the maximum number of records to return. There is also a size limit of 10 MB on the response that can’t be modified and that takes precedence over the number of records specified in the limit parameter. The response does include a threshold-breaching record if the 10 MB limit was reached.

    The range for limit is 1 to 100,000, with a default of 10.

    
    
  • iterator_type(IteratorType) / set_iterator_type(Option<IteratorType>):
    required: false
    

    Can be one of:

    • AT_SEQUENCE_NUMBER   –   Indicates that reading should start from the event sequence number specified jointly by the commitNum and opNum parameters.

    • AFTER_SEQUENCE_NUMBER   –   Indicates that reading should start right after the event sequence number specified jointly by the commitNum and opNum parameters.

    • TRIM_HORIZON   –   Indicates that reading should start at the last untrimmed record in the system, which is the oldest unexpired (not yet deleted) record in the change-log stream.

    • LATEST   –   Indicates that reading should start at the most recent record in the system, which is the latest unexpired (not yet deleted) record in the change-log stream.

    
    
  • commit_num(i64) / set_commit_num(Option<i64>):
    required: false
    

    The commit number of the starting record to read from the change-log stream. This parameter is required when iteratorType isAT_SEQUENCE_NUMBER or AFTER_SEQUENCE_NUMBER, and ignored when iteratorType is TRIM_HORIZON or LATEST.

    
    
  • op_num(i64) / set_op_num(Option<i64>):
    required: false
    

    The operation sequence number within the specified commit to start reading from in the change-log stream data. The default is 1.

    
    
  • encoding(Encoding) / set_encoding(Option<Encoding>):
    required: false
    

    If set to TRUE, Neptune compresses the response using gzip encoding.

    
    
• On success, responds with GetPropertygraphStreamOutput with field(s):
  • last_event_id(HashMap::<String, String>):

    Sequence identifier of the last change in the stream response.

    An event ID is composed of two fields: a commitNum, which identifies a transaction that changed the graph, and an opNum, which identifies a specific operation within that transaction:

  • last_trx_timestamp_in_millis(i64):

    The time at which the commit for the transaction was requested, in milliseconds from the Unix epoch.

  • format(String):

    Serialization format for the change records being returned. Currently, the only supported value is PG_JSON.

  • records(Vec::<PropertygraphRecord>):

    An array of serialized change-log stream records included in the response.

  • total_records(i32):

    The total number of records in the response.

• On failure, responds with SdkError<GetPropertygraphStreamError>

impl Client

pub fn get_propertygraph_summary(&self) -> GetPropertygraphSummaryFluentBuilder

Constructs a fluent builder for the GetPropertygraphSummary operation.

• The fluent builder is configurable:
  • mode(GraphSummaryType) / set_mode(Option<GraphSummaryType>):
    required: false
    

    Mode can take one of two values: BASIC (the default), and DETAILED.

    
    
• On success, responds with GetPropertygraphSummaryOutput with field(s):
  • status_code(Option<i32>):

    The HTTP return code of the request. If the request succeeded, the code is 200.

  • payload(Option<PropertygraphSummaryValueMap>):

    Payload containing the property graph summary response.

• On failure, responds with SdkError<GetPropertygraphSummaryError>

impl Client

pub fn get_rdf_graph_summary(&self) -> GetRDFGraphSummaryFluentBuilder

Constructs a fluent builder for the GetRDFGraphSummary operation.

• The fluent builder is configurable:
  • mode(GraphSummaryType) / set_mode(Option<GraphSummaryType>):
    required: false
    

    Mode can take one of two values: BASIC (the default), and DETAILED.

    
    
• On success, responds with GetRdfGraphSummaryOutput with field(s):
  • status_code(Option<i32>):

    The HTTP return code of the request. If the request succeeded, the code is 200.

  • payload(Option<RdfGraphSummaryValueMap>):

    Payload for an RDF graph summary response

• On failure, responds with SdkError<GetRDFGraphSummaryError>

impl Client

pub fn get_sparql_statistics(&self) -> GetSparqlStatisticsFluentBuilder

Constructs a fluent builder for the GetSparqlStatistics operation.

• The fluent builder takes no input, just send it.
• On success, responds with GetSparqlStatisticsOutput with field(s):
  • status(String):

    The HTTP return code of the request. If the request succeeded, the code is 200. See Common error codes for DFE statistics request for a list of common errors.

    When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the neptune-db:GetStatisticsStatus IAM action in that cluster.

  • payload(Option<Statistics>):

    Statistics for RDF data.

• On failure, responds with SdkError<GetSparqlStatisticsError>

impl Client

pub fn get_sparql_stream(&self) -> GetSparqlStreamFluentBuilder

Constructs a fluent builder for the GetSparqlStream operation.

• The fluent builder is configurable:
  • limit(i64) / set_limit(Option<i64>):
    required: false
    

    Specifies the maximum number of records to return. There is also a size limit of 10 MB on the response that can’t be modified and that takes precedence over the number of records specified in the limit parameter. The response does include a threshold-breaching record if the 10 MB limit was reached.

    The range for limit is 1 to 100,000, with a default of 10.

    
    
  • iterator_type(IteratorType) / set_iterator_type(Option<IteratorType>):
    required: false
    

    Can be one of:

    • AT_SEQUENCE_NUMBER   –   Indicates that reading should start from the event sequence number specified jointly by the commitNum and opNum parameters.

    • AFTER_SEQUENCE_NUMBER   –   Indicates that reading should start right after the event sequence number specified jointly by the commitNum and opNum parameters.

    • TRIM_HORIZON   –   Indicates that reading should start at the last untrimmed record in the system, which is the oldest unexpired (not yet deleted) record in the change-log stream.

    • LATEST   –   Indicates that reading should start at the most recent record in the system, which is the latest unexpired (not yet deleted) record in the change-log stream.

    
    
  • commit_num(i64) / set_commit_num(Option<i64>):
    required: false
    

    The commit number of the starting record to read from the change-log stream. This parameter is required when iteratorType isAT_SEQUENCE_NUMBER or AFTER_SEQUENCE_NUMBER, and ignored when iteratorType is TRIM_HORIZON or LATEST.

    
    
  • op_num(i64) / set_op_num(Option<i64>):
    required: false
    

    The operation sequence number within the specified commit to start reading from in the change-log stream data. The default is 1.

    
    
  • encoding(Encoding) / set_encoding(Option<Encoding>):
    required: false
    

    If set to TRUE, Neptune compresses the response using gzip encoding.

    
    
• On success, responds with GetSparqlStreamOutput with field(s):
  • last_event_id(HashMap::<String, String>):

    Sequence identifier of the last change in the stream response.

    An event ID is composed of two fields: a commitNum, which identifies a transaction that changed the graph, and an opNum, which identifies a specific operation within that transaction:

  • last_trx_timestamp_in_millis(i64):

    The time at which the commit for the transaction was requested, in milliseconds from the Unix epoch.

  • format(String):

    Serialization format for the change records being returned. Currently, the only supported value is NQUADS.

  • records(Vec::<SparqlRecord>):

    An array of serialized change-log stream records included in the response.

  • total_records(i32):

    The total number of records in the response.

• On failure, responds with SdkError<GetSparqlStreamError>

impl Client

pub fn list_gremlin_queries(&self) -> ListGremlinQueriesFluentBuilder

Constructs a fluent builder for the ListGremlinQueries operation.

• The fluent builder is configurable:
  • include_waiting(bool) / set_include_waiting(Option<bool>):
    required: false
    

    If set to TRUE, the list returned includes waiting queries. The default is FALSE;

    
    
• On success, responds with ListGremlinQueriesOutput with field(s):
  • accepted_query_count(Option<i32>):

    The number of queries that have been accepted but not yet completed, including queries in the queue.

  • running_query_count(Option<i32>):

    The number of Gremlin queries currently running.

  • queries(Option<Vec::<GremlinQueryStatus>>):

    A list of the current queries.

• On failure, responds with SdkError<ListGremlinQueriesError>

impl Client

pub fn list_loader_jobs(&self) -> ListLoaderJobsFluentBuilder

Constructs a fluent builder for the ListLoaderJobs operation.

• The fluent builder is configurable:
  • limit(i32) / set_limit(Option<i32>):
    required: false
    

    The number of load IDs to list. Must be a positive integer greater than zero and not more than 100 (which is the default).

    
    
  • include_queued_loads(bool) / set_include_queued_loads(Option<bool>):
    required: false
    

    An optional parameter that can be used to exclude the load IDs of queued load requests when requesting a list of load IDs by setting the parameter to FALSE. The default value is TRUE.

    
    
• On success, responds with ListLoaderJobsOutput with field(s):
  • status(String):

    Returns the status of the job list request.

  • payload(Option<LoaderIdResult>):

    The requested list of job IDs.

• On failure, responds with SdkError<ListLoaderJobsError>

impl Client

pub fn list_ml_data_processing_jobs( &self ) -> ListMLDataProcessingJobsFluentBuilder

Constructs a fluent builder for the ListMLDataProcessingJobs operation.

• The fluent builder is configurable:
  • max_items(i32) / set_max_items(Option<i32>):
    required: false
    

    The maximum number of items to return (from 1 to 1024; the default is 10).

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with ListMlDataProcessingJobsOutput with field(s):
  • ids(Option<Vec::<String>>):

    A page listing data processing job IDs.

• On failure, responds with SdkError<ListMLDataProcessingJobsError>

impl Client

pub fn list_ml_endpoints(&self) -> ListMLEndpointsFluentBuilder

Constructs a fluent builder for the ListMLEndpoints operation.

• The fluent builder is configurable:
  • max_items(i32) / set_max_items(Option<i32>):
    required: false
    

    The maximum number of items to return (from 1 to 1024; the default is 10.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with ListMlEndpointsOutput with field(s):
  • ids(Option<Vec::<String>>):

    A page from the list of inference endpoint IDs.

• On failure, responds with SdkError<ListMLEndpointsError>

impl Client

pub fn list_ml_model_training_jobs( &self ) -> ListMLModelTrainingJobsFluentBuilder

Constructs a fluent builder for the ListMLModelTrainingJobs operation.

• The fluent builder is configurable:
  • max_items(i32) / set_max_items(Option<i32>):
    required: false
    

    The maximum number of items to return (from 1 to 1024; the default is 10).

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with ListMlModelTrainingJobsOutput with field(s):
  • ids(Option<Vec::<String>>):

    A page of the list of model training job IDs.

• On failure, responds with SdkError<ListMLModelTrainingJobsError>

impl Client

pub fn list_ml_model_transform_jobs( &self ) -> ListMLModelTransformJobsFluentBuilder

Constructs a fluent builder for the ListMLModelTransformJobs operation.

• The fluent builder is configurable:
  • max_items(i32) / set_max_items(Option<i32>):
    required: false
    

    The maximum number of items to return (from 1 to 1024; the default is 10).

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
• On success, responds with ListMlModelTransformJobsOutput with field(s):
  • ids(Option<Vec::<String>>):

    A page from the list of model transform IDs.

• On failure, responds with SdkError<ListMLModelTransformJobsError>

impl Client

pub fn list_open_cypher_queries(&self) -> ListOpenCypherQueriesFluentBuilder

Constructs a fluent builder for the ListOpenCypherQueries operation.

• The fluent builder is configurable:
  • include_waiting(bool) / set_include_waiting(Option<bool>):
    required: false
    

    When set to TRUE and other parameters are not present, causes status information to be returned for waiting queries as well as for running queries.

    
    
• On success, responds with ListOpenCypherQueriesOutput with field(s):
  • accepted_query_count(Option<i32>):

    The number of queries that have been accepted but not yet completed, including queries in the queue.

  • running_query_count(Option<i32>):

    The number of currently running openCypher queries.

  • queries(Option<Vec::<GremlinQueryStatus>>):

    A list of current openCypher queries.

• On failure, responds with SdkError<ListOpenCypherQueriesError>

impl Client

pub fn manage_propertygraph_statistics( &self ) -> ManagePropertygraphStatisticsFluentBuilder

Constructs a fluent builder for the ManagePropertygraphStatistics operation.

• The fluent builder is configurable:
  • mode(StatisticsAutoGenerationMode) / set_mode(Option<StatisticsAutoGenerationMode>):
    required: false
    

    The statistics generation mode. One of: DISABLE_AUTOCOMPUTE, ENABLE_AUTOCOMPUTE, or REFRESH, the last of which manually triggers DFE statistics generation.

    
    
• On success, responds with ManagePropertygraphStatisticsOutput with field(s):
  • status(String):

    The HTTP return code of the request. If the request succeeded, the code is 200.

  • payload(Option<RefreshStatisticsIdMap>):

    This is only returned for refresh mode.

• On failure, responds with SdkError<ManagePropertygraphStatisticsError>

impl Client

pub fn manage_sparql_statistics(&self) -> ManageSparqlStatisticsFluentBuilder

Constructs a fluent builder for the ManageSparqlStatistics operation.

• The fluent builder is configurable:
  • mode(StatisticsAutoGenerationMode) / set_mode(Option<StatisticsAutoGenerationMode>):
    required: false
    

    The statistics generation mode. One of: DISABLE_AUTOCOMPUTE, ENABLE_AUTOCOMPUTE, or REFRESH, the last of which manually triggers DFE statistics generation.

    
    
• On success, responds with ManageSparqlStatisticsOutput with field(s):
  • status(String):

    The HTTP return code of the request. If the request succeeded, the code is 200.

  • payload(Option<RefreshStatisticsIdMap>):

    This is only returned for refresh mode.

• On failure, responds with SdkError<ManageSparqlStatisticsError>

impl Client

pub fn start_loader_job(&self) -> StartLoaderJobFluentBuilder

Constructs a fluent builder for the StartLoaderJob operation.

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

    The source parameter accepts an S3 URI that identifies a single file, multiple files, a folder, or multiple folders. Neptune loads every data file in any folder that is specified.

    The URI can be in any of the following formats.

    • s3://(bucket_name)/(object-key-name)

    • https://s3.amazonaws.com/(bucket_name)/(object-key-name)

    • https://s3.us-east-1.amazonaws.com/(bucket_name)/(object-key-name)

    The object-key-name element of the URI is equivalent to the prefix parameter in an S3 ListObjects API call. It identifies all the objects in the specified S3 bucket whose names begin with that prefix. That can be a single file or folder, or multiple files and/or folders.

    The specified folder or folders can contain multiple vertex files and multiple edge files.

    
    
  • format(Format) / set_format(Option<Format>):
    required: true
    

    The format of the data. For more information about data formats for the Neptune Loader command, see Load Data Formats.

    Allowed values

    • csv for the Gremlin CSV data format.

    • opencypher for the openCypher CSV data format.

    • ntriples for the N-Triples RDF data format.

    • nquads for the N-Quads RDF data format.

    • rdfxml for the RDF\XML RDF data format.

    • turtle for the Turtle RDF data format.

    
    
  • s3_bucket_region(S3BucketRegion) / set_s3_bucket_region(Option<S3BucketRegion>):
    required: true
    

    The Amazon region of the S3 bucket. This must match the Amazon Region of the DB cluster.

    
    
  • iam_role_arn(impl Into<String>) / set_iam_role_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) for an IAM role to be assumed by the Neptune DB instance for access to the S3 bucket. The IAM role ARN provided here should be attached to the DB cluster (see Adding the IAM Role to an Amazon Neptune Cluster.

    
    
  • mode(Mode) / set_mode(Option<Mode>):
    required: false
    

    The load job mode.

    Allowed values: RESUME, NEW, AUTO.

    Default value: AUTO.

    • RESUME   –   In RESUME mode, the loader looks for a previous load from this source, and if it finds one, resumes that load job. If no previous load job is found, the loader stops.

      The loader avoids reloading files that were successfully loaded in a previous job. It only tries to process failed files. If you dropped previously loaded data from your Neptune cluster, that data is not reloaded in this mode. If a previous load job loaded all files from the same source successfully, nothing is reloaded, and the loader returns success.

    • NEW   –   In NEW mode, the creates a new load request regardless of any previous loads. You can use this mode to reload all the data from a source after dropping previously loaded data from your Neptune cluster, or to load new data available at the same source.

    • AUTO   –   In AUTO mode, the loader looks for a previous load job from the same source, and if it finds one, resumes that job, just as in RESUME mode.

      If the loader doesn’t find a previous load job from the same source, it loads all data from the source, just as in NEW mode.

    
    
  • fail_on_error(bool) / set_fail_on_error(Option<bool>):
    required: false
    

    failOnError   –   A flag to toggle a complete stop on an error.

    Allowed values: “TRUE”, “FALSE”.

    Default value: “TRUE”.

    When this parameter is set to “FALSE”, the loader tries to load all the data in the location specified, skipping any entries with errors.

    When this parameter is set to “TRUE”, the loader stops as soon as it encounters an error. Data loaded up to that point persists.

    
    
  • parallelism(Parallelism) / set_parallelism(Option<Parallelism>):
    required: false
    

    The optional parallelism parameter can be set to reduce the number of threads used by the bulk load process.

    Allowed values:

    • LOW –   The number of threads used is the number of available vCPUs divided by 8.

    • MEDIUM –   The number of threads used is the number of available vCPUs divided by 2.

    • HIGH –   The number of threads used is the same as the number of available vCPUs.

    • OVERSUBSCRIBE –   The number of threads used is the number of available vCPUs multiplied by 2. If this value is used, the bulk loader takes up all available resources.

      This does not mean, however, that the OVERSUBSCRIBE setting results in 100% CPU utilization. Because the load operation is I/O bound, the highest CPU utilization to expect is in the 60% to 70% range.

    Default value: HIGH

    The parallelism setting can sometimes result in a deadlock between threads when loading openCypher data. When this happens, Neptune returns the LOAD_DATA_DEADLOCK error. You can generally fix the issue by setting parallelism to a lower setting and retrying the load command.

    
    
  • parser_configuration(impl Into<String>, impl Into<String>) / set_parser_configuration(Option<HashMap::<String, String>>):
    required: false
    

    parserConfiguration   –   An optional object with additional parser configuration values. Each of the child parameters is also optional:

    • namedGraphUri   –   The default graph for all RDF formats when no graph is specified (for non-quads formats and NQUAD entries with no graph).

      The default is https://aws.amazon.com/neptune/vocab/v01/DefaultNamedGraph.

    • baseUri   –   The base URI for RDF/XML and Turtle formats.

      The default is https://aws.amazon.com/neptune/default.

    • allowEmptyStrings   –   Gremlin users need to be able to pass empty string values(“”) as node and edge properties when loading CSV data. If allowEmptyStrings is set to false (the default), such empty strings are treated as nulls and are not loaded.

      If allowEmptyStrings is set to true, the loader treats empty strings as valid property values and loads them accordingly.

    
    
  • update_single_cardinality_properties(bool) / set_update_single_cardinality_properties(Option<bool>):
    required: false
    

    updateSingleCardinalityProperties is an optional parameter that controls how the bulk loader treats a new value for single-cardinality vertex or edge properties. This is not supported for loading openCypher data.

    Allowed values: “TRUE”, “FALSE”.

    Default value: “FALSE”.

    By default, or when updateSingleCardinalityProperties is explicitly set to “FALSE”, the loader treats a new value as an error, because it violates single cardinality.

    When updateSingleCardinalityProperties is set to “TRUE”, on the other hand, the bulk loader replaces the existing value with the new one. If multiple edge or single-cardinality vertex property values are provided in the source file(s) being loaded, the final value at the end of the bulk load could be any one of those new values. The loader only guarantees that the existing value has been replaced by one of the new ones.

    
    
  • queue_request(bool) / set_queue_request(Option<bool>):
    required: false
    

    This is an optional flag parameter that indicates whether the load request can be queued up or not.

    You don’t have to wait for one load job to complete before issuing the next one, because Neptune can queue up as many as 64 jobs at a time, provided that their queueRequest parameters are all set to “TRUE”. The queue order of the jobs will be first-in-first-out (FIFO).

    If the queueRequest parameter is omitted or set to “FALSE”, the load request will fail if another load job is already running.

    Allowed values: “TRUE”, “FALSE”.

    Default value: “FALSE”.

    
    
  • dependencies(impl Into<String>) / set_dependencies(Option<Vec::<String>>):
    required: false
    

    This is an optional parameter that can make a queued load request contingent on the successful completion of one or more previous jobs in the queue.

    Neptune can queue up as many as 64 load requests at a time, if their queueRequest parameters are set to “TRUE”. The dependencies parameter lets you make execution of such a queued request dependent on the successful completion of one or more specified previous requests in the queue.

    For example, if load Job-A and Job-B are independent of each other, but load Job-C needs Job-A and Job-B to be finished before it begins, proceed as follows:

    1. Submit load-job-A and load-job-B one after another in any order, and save their load-ids.

    2. Submit load-job-C with the load-ids of the two jobs in its dependencies field:

    Because of the dependencies parameter, the bulk loader will not start Job-C until Job-A and Job-B have completed successfully. If either one of them fails, Job-C will not be executed, and its status will be set to LOAD_FAILED_BECAUSE_DEPENDENCY_NOT_SATISFIED.

    You can set up multiple levels of dependency in this way, so that the failure of one job will cause all requests that are directly or indirectly dependent on it to be cancelled.

    
    
  • user_provided_edge_ids(bool) / set_user_provided_edge_ids(Option<bool>):
    required: false
    

    This parameter is required only when loading openCypher data that contains relationship IDs. It must be included and set to True when openCypher relationship IDs are explicitly provided in the load data (recommended).

    When userProvidedEdgeIds is absent or set to True, an :ID column must be present in every relationship file in the load.

    When userProvidedEdgeIds is present and set to False, relationship files in the load must not contain an :ID column. Instead, the Neptune loader automatically generates an ID for each relationship.

    It’s useful to provide relationship IDs explicitly so that the loader can resume loading after error in the CSV data have been fixed, without having to reload any relationships that have already been loaded. If relationship IDs have not been explicitly assigned, the loader cannot resume a failed load if any relationship file has had to be corrected, and must instead reload all the relationships.

    
    
• On success, responds with StartLoaderJobOutput with field(s):
  • status(String):

    The HTTP return code indicating the status of the load job.

  • payload(HashMap::<String, String>):

    Contains a loadId name-value pair that provides an identifier for the load operation.

• On failure, responds with SdkError<StartLoaderJobError>

impl Client

pub fn start_ml_data_processing_job( &self ) -> StartMLDataProcessingJobFluentBuilder

Constructs a fluent builder for the StartMLDataProcessingJob operation.

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

    A unique identifier for the new job. The default is an autogenerated UUID.

    
    
  • previous_data_processing_job_id(impl Into<String>) / set_previous_data_processing_job_id(Option<String>):
    required: false
    

    The job ID of a completed data processing job run on an earlier version of the data.

    
    
  • input_data_s3_location(impl Into<String>) / set_input_data_s3_location(Option<String>):
    required: true
    

    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.

    
    
  • processed_data_s3_location(impl Into<String>) / set_processed_data_s3_location(Option<String>):
    required: true
    

    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.

    
    
  • sagemaker_iam_role_arn(impl Into<String>) / set_sagemaker_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • processing_instance_type(impl Into<String>) / set_processing_instance_type(Option<String>):
    required: false
    

    The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.

    
    
  • processing_instance_volume_size_in_gb(i32) / set_processing_instance_volume_size_in_gb(Option<i32>):
    required: false
    

    The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.

    
    
  • processing_time_out_in_seconds(i32) / set_processing_time_out_in_seconds(Option<i32>):
    required: false
    

    Timeout in seconds for the data processing job. The default is 86,400 (1 day).

    
    
  • model_type(impl Into<String>) / set_model_type(Option<String>):
    required: false
    

    One of the two model types that Neptune ML currently supports: heterogeneous graph models (heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.

    
    
  • config_file_name(impl Into<String>) / set_config_file_name(Option<String>):
    required: false
    

    A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.

    
    
  • subnets(impl Into<String>) / set_subnets(Option<Vec::<String>>):
    required: false
    

    The IDs of the subnets in the Neptune VPC. The default is None.

    
    
  • security_group_ids(impl Into<String>) / set_security_group_ids(Option<Vec::<String>>):
    required: false
    

    The VPC security group IDs. The default is None.

    
    
  • volume_encryption_kms_key(impl Into<String>) / set_volume_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

    
    
  • s3_output_encryption_kms_key(impl Into<String>) / set_s3_output_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

    
    
• On success, responds with StartMlDataProcessingJobOutput with field(s):
  • id(Option<String>):

    The unique ID of the new data processing job.

  • arn(Option<String>):

    The ARN of the data processing job.

  • creation_time_in_millis(Option<i64>):

    The time it took to create the new processing job, in milliseconds.

• On failure, responds with SdkError<StartMLDataProcessingJobError>

impl Client

pub fn start_ml_model_training_job( &self ) -> StartMLModelTrainingJobFluentBuilder

Constructs a fluent builder for the StartMLModelTrainingJob operation.

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

    A unique identifier for the new job. The default is An autogenerated UUID.

    
    
  • previous_model_training_job_id(impl Into<String>) / set_previous_model_training_job_id(Option<String>):
    required: false
    

    The job ID of a completed model-training job that you want to update incrementally based on updated data.

    
    
  • data_processing_job_id(impl Into<String>) / set_data_processing_job_id(Option<String>):
    required: true
    

    The job ID of the completed data-processing job that has created the data that the training will work with.

    
    
  • train_model_s3_location(impl Into<String>) / set_train_model_s3_location(Option<String>):
    required: true
    

    The location in Amazon S3 where the model artifacts are to be stored.

    
    
  • sagemaker_iam_role_arn(impl Into<String>) / set_sagemaker_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role for SageMaker execution.This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • base_processing_instance_type(impl Into<String>) / set_base_processing_instance_type(Option<String>):
    required: false
    

    The type of ML instance used in preparing and managing training of ML models. This is a CPU instance chosen based on memory requirements for processing the training data and model.

    
    
  • training_instance_type(impl Into<String>) / set_training_instance_type(Option<String>):
    required: false
    

    The type of ML instance used for model training. All Neptune ML models support CPU, GPU, and multiGPU training. The default is ml.p3.2xlarge. Choosing the right instance type for training depends on the task type, graph size, and your budget.

    
    
  • training_instance_volume_size_in_gb(i32) / set_training_instance_volume_size_in_gb(Option<i32>):
    required: false
    

    The disk volume size of the training instance. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.

    
    
  • training_time_out_in_seconds(i32) / set_training_time_out_in_seconds(Option<i32>):
    required: false
    

    Timeout in seconds for the training job. The default is 86,400 (1 day).

    
    
  • max_hpo_number_of_training_jobs(i32) / set_max_hpo_number_of_training_jobs(Option<i32>):
    required: false
    

    Maximum total number of training jobs to start for the hyperparameter tuning job. The default is 2. Neptune ML automatically tunes the hyperparameters of the machine learning model. To obtain a model that performs well, use at least 10 jobs (in other words, set maxHPONumberOfTrainingJobs to 10). In general, the more tuning runs, the better the results.

    
    
  • max_hpo_parallel_training_jobs(i32) / set_max_hpo_parallel_training_jobs(Option<i32>):
    required: false
    

    Maximum number of parallel training jobs to start for the hyperparameter tuning job. The default is 2. The number of parallel jobs you can run is limited by the available resources on your training instance.

    
    
  • subnets(impl Into<String>) / set_subnets(Option<Vec::<String>>):
    required: false
    

    The IDs of the subnets in the Neptune VPC. The default is None.

    
    
  • security_group_ids(impl Into<String>) / set_security_group_ids(Option<Vec::<String>>):
    required: false
    

    The VPC security group IDs. The default is None.

    
    
  • volume_encryption_kms_key(impl Into<String>) / set_volume_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

    
    
  • s3_output_encryption_kms_key(impl Into<String>) / set_s3_output_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

    
    
  • enable_managed_spot_training(bool) / set_enable_managed_spot_training(Option<bool>):
    required: false
    

    Optimizes the cost of training machine-learning models by using Amazon Elastic Compute Cloud spot instances. The default is False.

    
    
  • custom_model_training_parameters(CustomModelTrainingParameters) / set_custom_model_training_parameters(Option<CustomModelTrainingParameters>):
    required: false
    

    The configuration for custom model training. This is a JSON object.

    
    
• On success, responds with StartMlModelTrainingJobOutput with field(s):
  • id(Option<String>):

    The unique ID of the new model training job.

  • arn(Option<String>):

    The ARN of the new model training job.

  • creation_time_in_millis(Option<i64>):

    The model training job creation time, in milliseconds.

• On failure, responds with SdkError<StartMLModelTrainingJobError>

impl Client

pub fn start_ml_model_transform_job( &self ) -> StartMLModelTransformJobFluentBuilder

Constructs a fluent builder for the StartMLModelTransformJob operation.

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

    A unique identifier for the new job. The default is an autogenerated UUID.

    
    
  • data_processing_job_id(impl Into<String>) / set_data_processing_job_id(Option<String>):
    required: false
    

    The job ID of a completed data-processing job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

    
    
  • ml_model_training_job_id(impl Into<String>) / set_ml_model_training_job_id(Option<String>):
    required: false
    

    The job ID of a completed model-training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

    
    
  • training_job_name(impl Into<String>) / set_training_job_name(Option<String>):
    required: false
    

    The name of a completed SageMaker training job. You must include either dataProcessingJobId and a mlModelTrainingJobId, or a trainingJobName.

    
    
  • model_transform_output_s3_location(impl Into<String>) / set_model_transform_output_s3_location(Option<String>):
    required: true
    

    The location in Amazon S3 where the model artifacts are to be stored.

    
    
  • sagemaker_iam_role_arn(impl Into<String>) / set_sagemaker_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • neptune_iam_role_arn(impl Into<String>) / set_neptune_iam_role_arn(Option<String>):
    required: false
    

    The ARN of an IAM role that provides Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will occur.

    
    
  • custom_model_transform_parameters(CustomModelTransformParameters) / set_custom_model_transform_parameters(Option<CustomModelTransformParameters>):
    required: false
    

    Configuration information for a model transform using a custom model. The customModelTransformParameters object contains the following fields, which must have values compatible with the saved model parameters from the training job:

    
    
  • base_processing_instance_type(impl Into<String>) / set_base_processing_instance_type(Option<String>):
    required: false
    

    The type of ML instance used in preparing and managing training of ML models. This is an ML compute instance chosen based on memory requirements for processing the training data and model.

    
    
  • base_processing_instance_volume_size_in_gb(i32) / set_base_processing_instance_volume_size_in_gb(Option<i32>):
    required: false
    

    The disk volume size of the training instance in gigabytes. The default is 0. Both input data and the output model are stored on disk, so the volume size must be large enough to hold both data sets. If not specified or 0, Neptune ML selects a disk volume size based on the recommendation generated in the data processing step.

    
    
  • subnets(impl Into<String>) / set_subnets(Option<Vec::<String>>):
    required: false
    

    The IDs of the subnets in the Neptune VPC. The default is None.

    
    
  • security_group_ids(impl Into<String>) / set_security_group_ids(Option<Vec::<String>>):
    required: false
    

    The VPC security group IDs. The default is None.

    
    
  • volume_encryption_kms_key(impl Into<String>) / set_volume_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

    
    
  • s3_output_encryption_kms_key(impl Into<String>) / set_s3_output_encryption_kms_key(Option<String>):
    required: false
    

    The Amazon Key Management Service (KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.

    
    
• On success, responds with StartMlModelTransformJobOutput with field(s):
  • id(Option<String>):

    The unique ID of the new model transform job.

  • arn(Option<String>):

    The ARN of the model transform job.

  • creation_time_in_millis(Option<i64>):

    The creation time of the model transform job, in milliseconds.

• On failure, responds with SdkError<StartMLModelTransformJobError>

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.