cspark 0.1.1

A Python SDK for interacting with Coherent Spark APIs

Coherent Spark Python SDK

The Coherent Spark Python SDK (currently in Beta) is designed to elevate the developer experience and provide convenient access to the Coherent Spark APIs.

👋 Just a heads-up:

This SDK is supported by the community. If you encounter any bumps while using it, please report them here by creating a new issue.

Installation

pip install -U cspark

🫣 This Python library requires Python 3.7+.

Usage

To use the SDK, you need a Coherent Spark account that lets you access the following:

• User authentication (API key, bearer token, or OAuth2.0 client credentials details)
• Base URL (including the environment and tenant name)
• Spark service URI (to locate a specific resource):
  • folder - the folder name (where the service is located)
  • service - the service name
  • version - the semantic version a.k.a revision number (e.g., 0.4.2)

A folder contains one or more services, which can have multiple versions. Technically speaking, when you're operating with a service, you're actually interacting with a specific version of that service (the latest version by default - unless specified otherwise).

Hence, there are various ways to indicate a Spark service URI:

• {folder}/{service}[?{version}] - version is optional.
• service/{service_id}
• version/{version_id}

IMPORTANT: Avoid using URL-encoded characters in the service URI.

Here's an example of how to execute a Spark service:

import cspark.sdk as Spark

spark = Spark.Client(env='my-env', tenant='my-tenant', api_key='my-api-key')
with spark.services as services:
    response = services.execute('my-folder/my-service', inputs={'value': 42})
    print(response.data)

Explore the examples and documentation folders to find out more about the SDK's capabilities.

Client Options

As shown in the examples above, the Spark.Client is your entry point to the SDK. It is quite flexible and can be configured with the following options:

Base URL

base_url (default: os.getenv['CSPARK_BASE_URL']): indicates the base URL of the Coherent Spark APIs. It should include the tenant and environment information.

spark = Spark.Client(base_url='https://excel.my-env.coherent.global/my-tenant')

Alternatively, a combination of env and tenant options can be used to construct the base URL.

spark = Spark.Client(env='my-env', tenant='my-tenant')

Authentication

The SDK supports three types of authentication schemes:

• api_key (default: os.getenv['CSPARK_API_KEY']): indicates the API key (also known as synthetic key), which is sensitive and should be kept secure.

spark = Spark.Client(api_key='my-api-key')

PRO TIP: The Spark platform supports public APIs that can be accessed without any form of authentication. In that case, you need to set api_key to open in order to create a Spark.Client.

• token (default: os.getenv['CSPARK_BEARER_TOKEN']): indicates the bearer token. It can be prefixed with 'Bearer' or not. A bearer token is usually valid for a limited time and should be refreshed periodically.

spark = Spark.Client(token='Bearer 123')

• oauth (default: os.getenv['CSPARK_CLIENT_ID'] and os.getenv['CSPARK_CLIENT_SECRET'] or os.getenv['CSPARK_OAUTH_PATH']): indicates the OAuth2.0 client credentials. You can either provide the client ID and secret directly or the file path to the JSON file containing the credentials.

spark = Spark.Client(oauth={'client_id': 'my-client-id', 'client_secret': 'my-client-secret'})
# or
spark = Spark.Client(oauth='path/to/oauth/credentials.json')

• timeout (default: 60000): indicates the maximum time (in milliseconds) that the client should wait for a response from Spark servers before timing out a request.

• max_retries (default: 2): indicates the maximum number of times that the client will retry a request in case of a temporary failure, such as an unauthorized response or a status code greater than 400.

• retry_interval (default: 1 second): indicates the delay between each retry.

• logger (default: True): enables or disables logs for the SDK.

spark = Spark.Client(logger=False)

Client Errors

SparkError is the base class for all custom errors thrown by the SDK. There are two types of it:

• SparkSdkError: usually thrown when an argument (user entry) fails to comply with the expected format. Because it's a client-side error, it will include the invalid entry as the cause in most cases.
• SparkApiError: when attempting to communicate with the API, the SDK will wrap any sort of failure (any error during the roundtrip) into SparkApiError, which includes the HTTP status code of the response and the request_id, a unique identifier of the request.

Some of the derived SparkApiError are:

Type	Status	When
InternetError	0	no internet access
BadRequestError	400	invalid request
UnauthorizedError	401	missing or invalid credentials
ForbiddenError	403	insufficient permissions
NotFoundError	404	resource not found
ConflictError	409	resource already exists
RateLimitError	429	too many requests
InternalServerError	500	server-side error
ServiceUnavailableError	503	server is down
UnknownApiError	None	unknown error

API Parity

The SDK aims to provide full parity with the Spark APIs over time. Below is a list of the currently supported APIs.

Authentication API - manages access tokens using OAuth2.0 Client Credentials flow:

• Authorization.oauth.retrieve_token(config) generates new access token.

Services API - manages Spark services:

• Spark.services.execute(uri, inputs) executes a Spark service.
• Spark.services.get_versions(uri) lists all the versions of a service.
• Spark.services.get_schema(uri) gets the schema of a service.
• Spark.services.get_metadata(uri) gets the metadata of a service.

Batches API - manages asynchronous batch processing:

• Spark.batches.describe() describes the batch pipelines across a tenant.
• Spark.batches.create(params, [options]) creates a new batch pipeline.
• Spark.batches.of(id) defines a client-side batch pipeline by ID.
• Spark.batches.of(id).get_info() gets the details of a batch pipeline.
• Spark.batches.of(id).get_status() gets the status of a batch pipeline.
• Spark.batches.of(id).push(data, [options]) adds input data to a batch pipeline.
• Spark.batches.of(id).pull([options]) retrieves the output data from a batch pipeline.
• Spark.batches.of(id).dispose() closes a batch pipeline.
• Spark.batches.of(id).cancel() cancels a batch pipeline.

PRO TIP: A service URI locator can be combined with other parameters to locate a specific service (or version of it) when it's not a string. For example, you may execute a public service using an object containing the folder, service, and public properties.

import cspark.sdk as Spark

spark = Spark.Client(env='my-env', tenant='my-tenant', api_key='open')

with spark.services as services:
    uri = Spark.UriParams(folder='my-folder', service='my-service', public=True)
    response = services.execute(uri, inputs={ 'value': 42 })
    print(response.data)

# The final URI in this case is:
#    'my-tenant/api/v3/public/folders/my-folder/services/my-service/execute'

See the Uri and UriParams classes for more details.

Contributing

Feeling motivated enough to contribute? Great! Your help is always appreciated.

Please read CONTRIBUTING.md for details on the code of conduct and the process for submitting pull requests.

Copyright and License

Apache-2.0