Client

neptunedata

R Documentation

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)