split-openfeature 0.0.1

The official Python Split Provider for OpenFeature

Split OpenFeature Provider for Python

Overview

This Provider is designed to allow the use of OpenFeature with Split, the platform for controlled rollouts, serving features to your users via the Split feature flag to manage your complete customer experience.

Compatibility

This SDK is compatible with Python 3 and higher.

Getting started

Below is a simple example that describes using the Split Provider. Please see the OpenFeature Documentation for details on how to use the OpenFeature SDK.

from open_feature import open_feature_api
from split_openfeature import SplitProvider

open_feature_api.set_provider(SplitProvider(api_key="YOUR_API_KEY"))

If you are more familiar with Split or want access to other initialization options, you can provide a Split client to the constructor. See the Split Java SDK Documentation for more information.

from open_feature import open_feature_api
from split_openfeature import SplitProvider
from splitio import get_factory

factory = get_factory("YOUR_API_KEY", config=config_file)
factory.block_until_ready(5)
open_feature_api.set_provider(SplitProvider(client=factory.client()))

where config_file is the Split config file you want to use

Use of OpenFeature with Split

After the initial setup you can use OpenFeature according to their documentation.

One important note is that the Split Provider requires a targeting key to be set. Often times this should be set when evaluating the value of a flag by setting an EvaluationContext which contains the targeting key. An example flag evaluation is

from open_feature import open_feature_api
from open_feature.evaluation_context.evaluation_context import EvaluationContext

client = open_feature_api.get_client("CLIENT_NAME")

context = EvaluationContext(targeting_key="TARGETING_KEY")
value = client.get_boolean_value("FLAG_NAME", False, context)

If the same targeting key is used repeatedly, the evaluation context may be set at the client level

context = EvaluationContext(targeting_key="TARGETING_KEY")
client.context = context

or at the OpenFeatureAPI level

from open_feature.open_feature_evaluation_context import set_api_evaluation_context

context = EvaluationContext(targeting_key="TARGETING_KEY")
set_api_evaluation_context(context)

If the context was set at the client or api level, it is not required to provide it during flag evaluation.

Submitting issues

The Split team monitors all issues submitted to this issue tracker. We encourage you to use this issue tracker to submit any bug reports, feedback, and feature enhancements. We'll do our best to respond in a timely manner.

Contributing

Please see Contributors Guide to find all you need to submit a Pull Request (PR).

License

Licensed under the Apache License, Version 2.0. See: Apache License.

About Split

Split is the leading Feature Delivery Platform for engineering teams that want to confidently deploy features as fast as they can develop them. Split’s fine-grained management, real-time monitoring, and data-driven experimentation ensure that new features will improve the customer experience without breaking or degrading performance. Companies like Twilio, Salesforce, GoDaddy and WePay trust Split to power their feature delivery.

To learn more about Split, contact [e-mail], or get started with feature flags for free at https://www.split.io/signup.

Split has built and maintains SDKs for:

• Java Github Docs
• Javascript Github Docs
• Node Github Docs
• .NET Github Docs
• Ruby Github Docs
• PHP Github Docs
• Python Github Docs
• GO Github Docs
• Android Github Docs
• iOS Github Docs

For a comprehensive list of open source projects visit our Github page.

Learn more about Split:

Visit split.io/product for an overview of Split, or visit our documentation at help.split.io for more detailed information.