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* - Refactor all modules calling into various api endpoints to coalesce into one
`client` objet.
- Add a builder object to facilitate creating the client with various options
- Remove specification of `iam_credentials_provider_type` and instead make use of
the default boto3 session for obtaining aws credentials (as we do for Sagemaker integration)
- Organize all tests using pytest to more easily filter on what tests should be run or not.
The neptune client can be build either directly with its constructor:
```python
from graph_notebook.neptune.client import Client
c = Client(host=foo)
c.status()
```
It can also be created using our builder class:
```python
from botocore.session import get_session
from graph_notebook.neptune.client import ClientBuilder
builder = ClientBuilder() \
.with_host(config.host) \
.with_port(config.port) \
.with_region(config.aws_region) \
.with_tls(config.ssl) \
.with_iam(get_session())
c = builder.build()
c.status()
```
The `Client` object has some components which are Neptune-specific, and some which are not:
- `sparql` - takes any SPARQL query and interprets whether it should be issued as type `query` or type `update`
- `sparql_query` - sends a query request to the configured SPARQL endpoint with the payload `{'query': 'YOUR QUERY'}`
- `sparql_update` - sends an update request to the configured SPARQL endpoint with the payload `{'update': 'YOUR QUERY'}`
- `do_sparql_request` - submits the given payload to the configured SPARQL endpoint
- `get_gremlin_connection` - returns a websocket connection to the configured gremlin endpoint.
- `gremlin_query` - obtains a new gremlin connection and submits the given query. The opened connection will be closed
after obtaining query results
- `gremlin_http_query` - executes the given gremlin query via http(s) instead of websocket.
- `gremlin_status` - returns the status of running gremlin queries on the configured Neptune endpoint. Takes an optional
`query_id` input to obtain the status of a specific query
- `sparql_explain` - obtains an explain query plan for the given SPARQL query (can be of type update or query)
- `sparql_status` - returns the status of running SPARQL queries on the configured Neptune endpoint. Takes an optional
`query_id` input to obtain the status of a specific query
- `sparql_cancel` - cancels the running SPARQL query with the provided query_id
- `gremlin_cancel` - cancels the running Gremlin query with the provided `query_id`
- `gremlin_explain` - obtains an explain query plan for a given Gremlin query
- `gremlin_profile` - obtains a profile query plan for a given Gremlin query
- `status` - retrieves the status of the configured Neptune endpoint
- `load` - submits a new bulk load job with the provided parameters.
- `load_status` - obtains the status of the bulk loader. Takes an optional `query_id` to obtain the status of a specific loader job
- `cancel_load` - cancels the provided bulk loader job id
- `initiate_reset` - obtains a token needed to execute a fast reset of your configured Neptune endpoint
- `perform_reset` - takes a token obtained from `initiate_reset` and performs the reset
- `dataprocessing_start` - starts a NeptuneML dataprocessing job with the provided parameters
- `dataprocessing_job_status` - obtains the status of a given dataprocessing job id
- `dataprocessing_status` - obtains the status of the configured Neptune dataprocessing endpoint
- `dataprocessing_stop` - stops the given dataprocessing job id
- `modeltraining_start` - starts a NeptuneML modeltraining job with the provided parameters
- `modeltraining_job_status` - obtains the status of a given modeltraining job id
- `modeltraining_status` - obtains the status of the configured Neptune modeltraining endpoint
- `modeltraining_stop` - stops the given modeltraining job id
- `endpoints_create` - creates a NeptuneML endpoint with the provided parameters
- `endpoints_status` - obtain the status of a given endpoint job
- `endpoints_delete` - delete a given endpoint id
- `endpoints` - obtain the status of all endpoints to the configured Neptune database
- `export` - helper function to call the Neptune exporter for NeptuneML. Note that this is not a Neptune endpoint.
- `export_status` - obtain the status of the configured exporter endpoint.- Loading branch information
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