Feature Management is a library for enabling/disabling features at runtime. Developers can use feature flags in simple use cases like conditional statement to more advanced scenarios like conditionally adding routes.
- Python 3.7 or later is required to use this package.
Install the Python feature management client library for Python with pip:
pip install featuremanagementYou can use feature flags from the Azure App Configuration service, a json file, or a dictionary.
from featuremanagement import FeatureManager
from azure.appconfiguration.provider import load
from azure.identity import DefaultAzureCredential
import os
endpoint = os.environ.get("APPCONFIGURATION_ENDPOINT_STRING")
# If no setting selector is set then feature flags with no label are loaded.
selects = {SettingSelector(key_filter=".appconfig.featureflag*")}
config = load(endpoint=endpoint, credential=DefaultAzureCredential(), selects=selects)
feature_manager = FeatureManager(config)
# Prints the value of the feature flag Alpha
print("Alpha is ", feature_manager.is_enabled("Alpha"))A Json file with the following format can be used to load feature flags.
{
"feature_management": {
"feature_flags": [
{
"id": "Alpha",
"description": "",
"enabled": "true",
"conditions": {
"client_filters": []
}
}
]
}
}Load feature flags from a json file.
from featuremanagement import FeatureManager
import json
import os
import sys
script_directory = os.path.dirname(os.path.abspath(sys.argv[0]))
f = open(script_directory + "/my_json_file.json", "r")
feature_flags = json.load(f)
feature_manager = FeatureManager(feature_flags)
# Returns the value of Alpha, based on the result of the feature filter
print("Alpha is ", feature_manager.is_enabled("Alpha"))from featuremanagement import FeatureManager
feature_flags = {
"feature_management": {
"feature_flags": [
{
"id": "Alpha",
"description": "",
"enabled": "true",
"conditions": {
"client_filters": []
}
}
]
}
}
feature_manager = FeatureManager(feature_flags)
# Is always true
print("Alpha is ", feature_manager.is_enabled("Alpha"))The FeatureManager is the main entry point for using feature flags. It is initialized with a dictionary of feature flags, and optional feature filters. The FeatureManager can then be used to check if a feature is enabled or disabled.
Feature Flags are objects that define how Feature Management enables/disables a feature. It contains an id and enabled property. The id is a string that uniquely identifies the feature flag. The enabled property is a boolean that indicates if the feature flag is enabled or disabled. The conditions object contains a property client_filters which is a list of FeatureFilter objects that are used to determine if the feature flag is enabled or disabled. The Feature Filters only run if the feature flag is enabled.
The full schema for a feature Flag can be found here.
{
"id": "Alpha",
"enabled": "true",
"conditions": {
"client_filters": [
{
"name": "MyFilter",
"parameters": {
...
}
}
]
}
}This object is passed into the FeatureManager when it is initialized.
Feature filters enable dynamic evaluation of feature flags. The Python feature management library includes two built-in filters:
Microsoft.TimeWindow- Enables a feature flag based on a time window.Microsoft.Targeting- Enables a feature flag based on a list of users, groups, or rollout percentages.
The Time Window Filter enables a feature flag based on a time window. It has two parameters:
Start- The start time of the time window.End- The end time of the time window.
{
"name": "Microsoft.TimeWindow",
"parameters": {
"Start": "2020-01-01T00:00:00Z",
"End": "2020-12-31T00:00:00Z"
}
}Both parameters are optional, but at least one is required. The time window filter is enabled after the start time and before the end time. If the start time is not specified, it is enabled immediately. If the end time is not specified, it will remain enabled after the start time.
Targeting is a feature management strategy that enables developers to progressively roll out new features to their user base. The strategy is built on the concept of targeting a set of users known as the target audience. An audience is made up of specific users, groups, excluded users/groups, and a designated percentage of the entire user base. The groups that are included in the audience can be broken down further into percentages of their total members.
The following steps demonstrate an example of a progressive rollout for a new 'Beta' feature:
- Individual users Jeff and Alicia are granted access to the Beta
- Another user, Mark, asks to opt-in and is included.
- Twenty percent of a group known as "Ring1" users are included in the Beta.
- The number of "Ring1" users included in the beta is bumped up to 100 percent.
- Five percent of the user base is included in the beta.
- The rollout percentage is bumped up to 100 percent and the feature is completely rolled out.
This strategy for rolling out a feature is built in to the library through the included Microsoft.Targeting feature filter.
The Targeting Filter provides the capability to enable a feature for a target audience. The filter parameters include an Audience object which describes users, groups, excluded users/groups, and a default percentage of the user base that should have access to the feature. The Audience object contains the following fields:
Users- A list of users that the feature flag is enabled for.Groups- A list of groups that the feature flag is enabled for and a rollout percentage for each group.Name- The name of the group.RolloutPercentage- A percentage value that the feature flag is enabled for in the given group.
DefaultRolloutPercentage- A percentage value that the feature flag is enabled for.Exclusion- An object that contains a list of users and groups that the feature flag is disabled for.Users- A list of users that the feature flag is disabled for.Groups- A list of groups that the feature flag is disabled for.
{
"name": "Microsoft.Targeting",
"parameters": {
"Audience": {
"Users": ["user1", "user2"],
"Groups": [
{
"Name": "group1",
"RolloutPercentage": 100
}
],
"DefaultRolloutPercentage": 50,
"Exclusion": {
"Users": ["user3"],
"Groups": ["group2"]
}
}
}
}You can provide the current user info through kwargs when calling isEnabled.
from featuremanagement import FeatureManager, TargetingContext
# Returns true, because user1 is in the Users list
feature_manager.is_enabled("Beta", TargetingContext(user_id="user1", groups=["group1"]))
# Returns false, because group2 is in the Exclusion.Groups list
feature_manager.is_enabled("Beta", TargetingContext(user_id="user1", groups=["group2"]))
# Has a 50% chance of returning true, but will be conisistent for the same user
feature_manager.is_enabled("Beta", TargetingContext(user_id="user4"))You can also create your own feature filters by implementing the FeatureFilter interface.
class MyCustomFilter(FeatureFilter):
def evaluate(self, context, **kwargs):
...
return TrueThey can then be passed into the FeatureManager when it is initialized. By default, the name of a feature filter is the name of the class. You can override this by setting a class attribute alias to the modified class name.
feature_manager = FeatureManager(feature_flags, feature_filters={MyCustomFilter(), MyOtherFilter()})The evaluate method is called when checking if a feature flag is enabled. The context parameter contains information about the feature filter from the parameters field of the feature filter. Any additional parameters can be passed in as keyword arguments when calling is_enabled.
{
"name": "CustomFilter",
"parameters": {
...
}
}You can modify the name of a feature flag by using the @FeatureFilter.alias decorator. The alias overrides the name of the feature filter and needs to match the name of the feature filter in the feature flag json.
@FeatureFilter.alias("AliasFilter")
class MyCustomFilter(FeatureFilter):
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