neptunedata: Amazon NeptuneData

In paws.database: 'Amazon Web Services' Database Services

Amazon NeptuneData

Description

Neptune Data API

The Amazon Neptune data API provides SDK support for more than 40 of Neptune's data operations, including data loading, query execution, data inquiry, and machine learning. It supports the Gremlin and openCypher query languages, and is available in all SDK languages. It automatically signs API requests and greatly simplifies integrating Neptune into your applications.

Usage

neptunedata(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config	Optional configuration of credentials, endpoint, and/or region. credentials: creds: access_key_id: AWS access key ID secret_access_key: AWS secret access key session_token: AWS temporary session token profile: The name of a profile to use. If not given, then the default profile is used. anonymous: Set anonymous credentials. endpoint: The complete URL to use for the constructed client. region: The AWS Region used in instantiating the client. close_connection: Immediately close all HTTP connections. timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠. sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html
credentials	Optional credentials shorthand for the config parameter creds: access_key_id: AWS access key ID secret_access_key: AWS secret access key session_token: AWS temporary session token profile: The name of a profile to use. If not given, then the default profile is used. anonymous: Set anonymous credentials.
endpoint	Optional shorthand for complete URL to use for the constructed client.
region	Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- neptunedata(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

cancel_gremlin_query	Cancels a Gremlin query
cancel_loader_job	Cancels a specified load job
cancel_ml_data_processing_job	Cancels a Neptune ML data processing job
cancel_ml_model_training_job	Cancels a Neptune ML model training job
cancel_ml_model_transform_job	Cancels a specified model transform job
cancel_open_cypher_query	Cancels a specified openCypher query
create_ml_endpoint	Creates a new Neptune ML inference endpoint that lets you query one specific model that the model-training process constructed
delete_ml_endpoint	Cancels the creation of a Neptune ML inference endpoint
delete_propertygraph_statistics	Deletes statistics for Gremlin and openCypher (property graph) data
delete_sparql_statistics	Deletes SPARQL statistics
execute_fast_reset	The fast reset REST API lets you reset a Neptune graph quicky and easily, removing all of its data
execute_gremlin_explain_query	Executes a Gremlin Explain query
execute_gremlin_profile_query	Executes a Gremlin Profile query, which runs a specified traversal, collects various metrics about the run, and produces a profile report as output
execute_gremlin_query	This commands executes a Gremlin query
execute_open_cypher_explain_query	Executes an openCypher explain request
execute_open_cypher_query	Executes an openCypher query
get_engine_status	Retrieves the status of the graph database on the host
get_gremlin_query_status	Gets the status of a specified Gremlin query
get_loader_job_status	Gets status information about a specified load job
get_ml_data_processing_job	Retrieves information about a specified data processing job
get_ml_endpoint	Retrieves details about an inference endpoint
get_ml_model_training_job	Retrieves information about a Neptune ML model training job
get_ml_model_transform_job	Gets information about a specified model transform job
get_open_cypher_query_status	Retrieves the status of a specified openCypher query
get_propertygraph_statistics	Gets property graph statistics (Gremlin and openCypher)
get_propertygraph_stream	Gets a stream for a property graph
get_propertygraph_summary	Gets a graph summary for a property graph
get_rdf_graph_summary	Gets a graph summary for an RDF graph
get_sparql_statistics	Gets RDF statistics (SPARQL)
get_sparql_stream	Gets a stream for an RDF graph
list_gremlin_queries	Lists active Gremlin queries
list_loader_jobs	Retrieves a list of the loadIds for all active loader jobs
list_ml_data_processing_jobs	Returns a list of Neptune ML data processing jobs
list_ml_endpoints	Lists existing inference endpoints
list_ml_model_training_jobs	Lists Neptune ML model-training jobs
list_ml_model_transform_jobs	Returns a list of model transform job IDs
list_open_cypher_queries	Lists active openCypher queries
manage_propertygraph_statistics	Manages the generation and use of property graph statistics
manage_sparql_statistics	Manages the generation and use of RDF graph statistics
start_loader_job	Starts a Neptune bulk loader job to load data from an Amazon S3 bucket into a Neptune DB instance
start_ml_data_processing_job	Creates a new Neptune ML data processing job for processing the graph data exported from Neptune for training
start_ml_model_training_job	Creates a new Neptune ML model training job
start_ml_model_transform_job	Creates a new model transform job

Examples

## Not run: 
svc <- neptunedata()
svc$cancel_gremlin_query(
  Foo = 123
)

## End(Not run)

paws.database documentation built on Sept. 12, 2024, 6:37 a.m.