fb-python-sdk 1.1.2

A Python SDK for FeatBit plateform

FeatBit Server-Side SDK for Python

Introduction

This is the Python Server-Side SDK for the 100% open-source feature flags management platform FeatBit.

The FeatBit Server-Side SDK for Python is designed primarily for use in multi-user systems such as web servers and applications.

Data synchonization

We use websocket to make the local data synchronized with the FeatBit server, and then store them in memory by default. Whenever there is any change to a feature flag or its related data, this change will be pushed to the SDK and the average synchronization time is less than 100 ms. Be aware the websocket connection may be interrupted due to internet outage, but it will be resumed automatically once the problem is gone.

If you want to use your own data source, see Offline Mode.

Get Started

Installation

install the sdk in using pip, this version of the SDK is compatible with Python 3.6 through 3.11.

pip install fb-python-sdk

Quick Start

Note that the env_secret, streaming_url and event_url are required to initialize the SDK.

The following code demonstrates basic usage of the SDK.

from fbclient import get, set_config
from fbclient.config import Config

env_secret = '<replace-with-your-env-secret>'
event_url = 'http://localhost:5100'
streaming_url = 'ws://localhost:5100'

set_config(Config(env_secret, event_url, streaming_url))
client = get()

if client.initialize:
    flag_key = '<replace-with-your-flag-key>'
    user_key = 'bot-id'
    user_name = 'bot'
    user = {'key': user_key, 'name': user_name}
    detail = client.variation_detail(flag_key, user, default=None)
    print(f'flag {flag_key} returns {detail.variation} for user {user_key}, reason: {detail.reason}')

# ensure that the SDK shuts down cleanly and has a chance to deliver events to FeatBit before the program exits
client.stop()

Examples

• Python Demo

FBClient

Applications SHOULD instantiate a single FBClient instance for the lifetime of the application. In the case where an application needs to evaluate feature flags from different environments, you may create multiple clients, but they should still be retained for the lifetime of the application rather than created per request or per thread.

Bootstrapping

The bootstrapping is in fact the call of constructor of FBClient, in which the SDK will be initialized and connect to FeatBit.

The constructor will return when it successfully connects, or when the timeout(default: 15 seconds) expires, whichever comes first. If it has not succeeded in connecting when the timeout elapses, you will receive the client in an uninitialized state where feature flags will return default values; it will still continue trying to connect in the background unless there has been a network error or you close the client(using stop()). You can detect whether initialization has succeeded by calling initialize().

The best way to use the SDK as a singleton, first make sure you have called fbclient.set_config() at startup time. Then fbclient.get() will return the same shared fbclient.client.FBClient instance each time. The client will be initialized if it runs first time.

from fbclient.config import Config
from fbclient import get, set_config 

set_config(Config(env_secret, event_url, streaming_url))
client = get()

if client.initialize:
    # the client is ready

You can also manage your fbclient.client.FBClient, the SDK will be initialized if you call fbclient.client.FBClient constructor. With constructor, you can set the timeout for initialization, the default value is 15 seconds.

from fbclient.config import Config
from fbclient.client import FBClient

client = FBClient(Config(env_secret, event_url, streaming_url), start_wait=15)

if client.initialize:
    # the client is ready

If you prefer to have the constructor return immediately, and then wait for initialization to finish at some other point, you can use fbclient.client.fbclient.update_status_provider object, which provides an asynchronous way, as follows:

from fbclient.config import Config
from fbclient.client import FBClient

client = FBClient(Config(env_secret), start_wait=0)
if client.update_status_provider.wait_for_OKState():
    # the client is ready

It's possible to set a timeout in seconds for the wait_for_OKState method. If the timeout is reached, the method will return False and the client will still be in an uninitialized state. If you do not specify a timeout, the method will wait indefinitely.

To check if the client is ready is optional. Even if the client is not ready, you can still evaluate feature flags, but the default value will be returned if SDK is not yet initialized.

FBUser

A dictionary of attributes that can affect flag evaluation, usually corresponding to a user of your application. This object contains built-in properties(key, name). The key and name are required. The key must uniquely identify each user; this could be a username or email address for authenticated users, or a ID for anonymous users. The name is used to search your user quickly. You may also define custom properties with arbitrary names and values. For instance, the custom key should be a string; the custom value should be a string, number or boolean value

user = {'key': user_key, 'name': user_name, 'age': age}

Evaluation

SDK calculates the value of a feature flag for a given user, and returns a flag vlaue/an object that describes the way that the value was determined.

if client.initialize:
    user = {'key': user_key, 'name': user_name, 'age': age}
    # evaluate the flag value
    flag_value = client.variation(flag_key, user, default_value)
    # evaluate the flag value and get the detail
    detail = client.variation_detail(flag_key, user, default=None)

If you would like to get variations of all feature flags in a special environment, you can use fbclient.client.FBClient.get_all_latest_flag_variations, SDK will return fbclient.common_types.AllFlagStates, that explain the details of all feature flags. fbclient.common_types.AllFlagStates.get() returns the detail of a given feature flag key.

if client.initialize:
    user = {'key': user_key, 'name': user_name}
    all_flag_values = client.get_all_latest_flag_variations(user)
    detail = all_flag_values.get(flag_key, default=None)

Note that if evaluation called before Go SDK client initialized, you set the wrong flag key/user for the evaluation or the related feature flag is not found, SDK will return the default value you set. The fbclient.common_types.EvalDetail will explain the details of the latest evaluation including error raison.

Offline Mode

In some situations, you might want to stop making remote calls to FeatBit. Here is how:

config = Config(env_secret, event_url, streaming_url, offline=True)

When you put the SDK in offline mode, no insight message is sent to the server and all feature flag evaluations return fallback values because there are no feature flags or segments available. If you want to use your own data source, SDK allows users to populate feature flags and segments data from a JSON string. Here is an example: fbclient_test_data.json.

The format of the data in flags and segments is defined by FeatBit and is subject to change. Rather than trying to construct these objects yourself, it's simpler to request existing flags directly from the FeatBit server in JSON format and use this output as the starting point for your file. Here's how:

# replace http://localhost:5100 with your evaluation server url
curl -H "Authorization: <your-env-secret>" http://localhost:5100/api/public/sdk/server/latest-all > featbit-bootstrap.json

Then you can use this file to initialize the SDK in offline mode:

// first load data from file and then
client.initialize_from_external_json(json)

Experiments (A/B/n Testing)

We support automatic experiments for pageviews and clicks, you just need to set your experiment on our SaaS platform, then you should be able to see the result in near real time after the experiment is started.

In case you need more control over the experiment data sent to our server, we offer a method to send custom event.

client.track_metric(user, event_name, numeric_value);

numeric_value is not mandatory, the default value is 1.

Make sure track_metric is called after the related feature flag is evaluated by simply calling variation or variation_detail otherwise, the custom event may not be included into the experiment result.

Getting support

• If you have a specific question about using this sdk, we encourage you to ask it in our slack.
• If you encounter a bug or would like to request a feature, submit an issue.

See Also

• Connect To Python Sdk