Spring Cloud for Azure feature management client library for Java

Article
11/05/2024

Feature Management

Feature flags provide a way for Spring Boot applications to turn features on or off dynamically. Developers can use feature flags in simple use cases like conditional statement to more advanced scenarios like conditionally adding routes. Feature Flags are not dependent of any spring-cloud-azure dependencies, but may be used in conjunction with spring-cloud-azure-appconfiguration-config.

Here are some of the benefits of using this library:

A common convention for feature management
Low barrier-to-entry
- Supports application.yml file feature flag setup
Feature Flag lifetime management
- Configuration values can change in real-time, feature flags can be consistent across the entire request

Feature Flags

Feature flags are composed of two parts, a name and a list of feature-filters that are used to turn the feature on.

Feature Filters

Feature filters define a scenario for when a feature should be enabled. When a feature is evaluated for whether it is on or off, its list of feature-filters are traversed until one of the filters decides the feature should be enabled. At this point the feature is considered enabled and traversal through the feature filters stops. If no feature filter indicates that the feature should be enabled, then it will be considered disabled.

As an example, a Microsoft Edge browser feature filter could be designed. This feature filter would activate any features it is attached to as long as an HTTP request is coming from Microsoft Edge.

Registration

The Spring Configuration system is used to determine the state of feature flags. Any system can be used to have them read in, such as application.yml, spring-cloud-azure-appconfiguration-config and more.

Feature Flag Declaration

The feature management library supports application.yml or bootstrap.yml as a feature flag source. Below we have an example of the format used to set up feature flags in a application.yml file.

feature-management:
  feature-flags:
    feature-t: false
    feature-u:
      enabled-for:
        -
          name: Random
    feature-v:
      enabled-for:
        -
          name: Microsoft.TimeWindow
          parameters:
            start: "Wed, 01 May 2019 13:59:59 GMT"
            end: "Mon, 01 July 2019 00:00:00 GMT"
    feature-w:
      evaluate: false
      enabled-for:
        -
          name: AlwaysOnFilter

The feature-management section of the YAML document is used by convention to load feature flags. In the section above, we see that we have provided three different features. Features define their filters using the enabled-for property. We can see that feature feature-t is set to false with no filters set. feature-t will always return false, this can also be done for true. feature-u which has only one feature filter Random which does not require any configuration so it only has the name property. feature-v it specifies a feature filter named TimeWindow. This is an example of a configurable feature filter. We can see in the example that the filter has a parameter's property. This is used to configure the filter. In this case, the start and end times for the feature to be active are configured.

The AlwaysOnFilter is a Filter that always evaluates as true. This filter can be used to turn this feature flag on, without removing the other feature filters. The evaluate field is used to stop the evaluation of the feature filters, and results in the feature flag to always return false.

Supported properties

Name	Description	Required	Default
spring.cloud.azure.feature.management.fail-fast	Whether throw RuntimeException or not when exception occurs	No	true

Consumption

The simplest use case for feature flags is to do a conditional check for whether a feature is enabled to take different paths in code. The use cases grow when additional using spring-cloud-azure-feature-flag-web to manage web based features.

Feature Check

The basic form of feature management is checking if a feature is enabled and then performing actions based on the result. This is done through the autowiring FeatureManager and calling it's isEnabledAsync method.

@Autowired
FeatureManager featureManager;

if(featureManager.isEnabledAsync("feature-t").block()) {
    // Do Something
}

FeatureManager can also be accessed by @Component classes.

Controllers

When using the Feature Management Web library you can require that a given feature is enabled in order to execute. This can be done by using the @FeatureOn annotation.

@GetMapping("/featureT")
@FeatureGate(feature = "feature-t")
@ResponseBody
public String featureT() {
    ...
}

The featureT endpoint can only be accessed if "feature-t" is enabled.

Disabled Action Handling

When a controller is blocked because the feature it specifies is disabled, IDisabledFeaturesHandler will be invoked. By default, a HTTP 404 is returned. This can be overridden using implementing IDisabledFeaturesHandler.

@Component
public class DisabledFeaturesHandler implements IDisabledFeaturesHandler{

    @Override
    public HttpServletResponse handleDisabledFeatures(HttpServletRequest request, HttpServletResponse response) {
        ...
        return response;
    }

}

Routing

Certain routes may expose application capabilities that are gated by features. These routes can redirected if a feature is disabled to another endpoint.

@GetMapping("/featureT")
@FeatureGate(feature = "feature-t", fallback= "/oldEndpoint")
@ResponseBody
public String featureT() {
    ...
}

@GetMapping("/oldEndpoint")
@ResponseBody
public String oldEndpoint() {
    ...
}

Implementing a Feature Filter

Creating a feature filter provides a way to enable features based on criteria that you define. To implement a feature filter, the FeatureFilter interface must be implemented. FeatureFilter has a single method evaluate. When a feature specifies that it can be enabled with a feature filter, the evaluate method is called. If evaluate returns true it means the feature should be enabled. If false it will continue evaluating the Feature's filters until one returns true. If all return false then the feature is off.

Feature filters are found by being defined as @Component where there name matches the expected filter defined in the configuration.

@Component("Random")
public class Random implements FeatureFilter {

    @Override
    public boolean evaluate(FeatureFilterEvaluationContext context) {
        double chance = Double.valueOf((String) context.getParameters().get("chance"));
        return Math.random() > chance/100;
    }

}

Parameterized Feature Filters

Some feature filters require parameters to decide whether a feature should be turned on or not. For example a browser feature filter may turn on a feature for a certain set of browsers. It may be desired that Edge and Chrome browsers enable a feature, while FireFox does not. To do this a feature filter can be designed to expect parameters. These parameters would be specified in the feature configuration, and in code would be accessible via the FeatureFilterEvaluationContext parameter of evaluate. FeatureFilterEvaluationContext has a property parameters which is a HashMap<String, Object>.

Request Based Features/Snapshot

There are scenarios which require the state of a feature to remain consistent during the lifetime of a request. The values returned from the standard FeatureManager may change if the configuration source which it is pulling from is updated during the request. This can be prevented by using FeatureManagerSnapshot and @FeatureOn( snapshot = true ). FeatureManagerSnapshot can be retrieved in the same manner as FeatureManager. FeatureManagerSnapshot calls FeatureManager, but it caches the first evaluated state of a feature during a request and will return the same state of a feature during its lifetime.

Built-In Feature Filters

There are a few feature filters that come with the azure-spring-cloud-feature-management package. These feature filters are not added automatically, but can be referenced and registered as soon as the package is registered.

Each of the built-in feature filters have their own parameters. Here is the list of feature filters along with examples.

PercentageFilter

This filter provides the capability to enable a feature based on a set percentage.

feature-management:
  feature-flags:
    feature-v:
      enabled-for:
        -
          name: PercentageFilter
          parameters:
            percentage-filter-setting: 50

TimeWindowFilter

This filter provides the capability to enable a feature based on a time window. If only end is specified, the feature will be considered enabled until that time. If only start is specified, the feature will be considered enabled at all points after that time. If both are specified the feature will be considered enabled between the two times.

feature-management:
  feature-flags:
    feature-v:
      enabled-for:
        -
          name: Microsoft.TimeWindow
          parameters:
            start: "Wed, 01 May 2019 13:59:59 GMT"
            end: "Mon, 01 July 2019 00:00:00 GMT"

The time window can be configured to recur periodically. This can be useful in the scenario where you have to enable/disable a feature during low or high traffic periods of a day or for certain days of the week. To expand the individual time window to recurring time windows, the recurrence rule should be specified in the recurrence parameter.

feature-management:
  feature-flags:
    feature-v:
      enabled-for:
        -
          name: Microsoft.TimeWindow
          parameters:
            start: "Fri, 22 Mar 2024 20:00:00 GMT"
            end: "Sat, 23 Mar 2024 02:00:00 GMT"
            recurrence:
              pattern:
                type: "Daily"
                interval: 1
              range:
                type: "Numbered"
                numberOfOccurrences: 3

To create a recurrence rule, you need to specify 3 parts: start, end and recurrence. The start and end parameters define the time window which need to recur periodically. The recurrence parameter is made up of two parts: pattern (how often the time window will repeat) and range (for how long the recurrence pattern will repeat). To create a recurrence rule, you must specify both pattern and range. Any pattern type can work with any range type. The time zone offset of the start property will apply to the recurrence settings.

Note: start must be a valid first occurrence which fits the recurrence pattern. For example, if we define to repeat on every other Monday and Tuesday, then the start time should be in Monday or Tuesday.
Additionally, the duration of the time window cannot be longer than how frequently it occurs. For example, it is invalid to have a 25-hour time window recur every day.

Recurrence Pattern

There are two possible recurrence pattern types: Daily and Weekly.

Daily causes an event to repeat based on a number of days between each occurrence. For example, "every day" or "every 3 days".

Weekly causes an event to repeat on the same day or days of the week, based on the number of weeks between each set of occurrences. For example, "every Monday" or "every other Friday".

Parameters

Property	Relevance	Description
type	Required	`Daily`/`Weekly`.
interval	Optional	Specifies the interval between each start time of occurrence. Default value is 1. For example, if the interval is 2 with `Daily` type, and current occurrence is 2:00 AM ~ 3:00 AM on 2024/05/11, then the next occurrence should be 2:00 AM ~ 3:00 AM on 2024/05/13
daysOfWeek	Required/Optional	Specifies on which day(s) of the week the event occurs. It's required when `Weekly` type, negatively for `Daily` type.
firstDayOfWeek	Optional	Specifies which day is considered the first day of the week. Default value is `Sunday`. Negatively for `Daily` type.

Example

Daily

The following example will repeat from 2:00 AM to 3:00 AM on every 2 days

start: "Mon, 13 May 2024 02:00:00 GMT"
end: "Mon, 13 May 2024 03:00:00 GMT"
recurrence:
  pattern:
    type: "Daily"
    interval: 2
  range:
    type: "NoEnd"

Weekly

The following example will repeat from 2:00 AM to 3:00 AM on every other Monday and Tuesday

start: "Mon, 13 May 2024 02:00:00 GMT"
end: "Mon, 13 May 2024 03:00:00 GMT"
recurrence:
  pattern:
    type: "Weekly"
    interval: 2
    daysOfWeek: 
      - Monday
      - Tuesday
  range:
    type: "NoEnd"

The following example shows a time window starts on one day and ends on another, repeat every week.

start: "Mon, 13 May 2024 02:00:00 GMT"
end: "Mon, 14 May 2024 03:00:00 GMT"
recurrence:
  pattern:
    type: "Weekly"
    interval: 1
    daysOfWeek: 
      - Monday
  range:
    type: "NoEnd"

Recurrence Range

There are three possible recurrence range type: NoEnd, EndDate and Numbered.

Parameter

Property	Relevance	Description
type	Required	`NoEnd`/`EndDate`/`Numbered`.
endDate	Required/Optional	Specifies the date time to stop applying the pattern. Note that as long as the start time of the last occurrence falls before the end date, the end time of that occurrence is allowed to extend beyond it. It's required for `EndDate` type, negatively for `NoEnd` and `Numbered` type.
NumberOfOccurrences	Required/Optional	Specifies the number of days that it will occur. It's required for `Numbered` type, negatively for `NoEnd` and `EndDate` type.

Example
- NoEnd
  
  The NoEnd range causes the recurrence to occur indefinitely.
  
  The following example will repeat from 6:00 PM to 8:00 PM every day.
```
  start: "Fri, 22 Mar 2024 18:00:00 GMT"
  end: "Fri, 22 Mar 2024 20:00:00 GMT"
  recurrence:
    pattern:
      type: "Daily"
      interval: 1
    range:
      type: "NoEnd"
```
- EndDate
  
  The EndDate range causes the time window to occur on all days that fit the applicable pattern until the end date.
  
  The following example will repeat from 6:00 PM to 8:00 PM every day until the last occurrence happens on April 1st, 2024.
```
start: "Fri, 22 Mar 2024 18:00:00 GMT"
end: "Fri, 22 Mar 2024 20:00:00 GMT"
recurrence:
  pattern:
    type: "Daily"
    interval: 1
  range:
    type: "EndDate"
    endDate: "Mon, 1 Apr 2024 20:00:00 GMT"
```
- Numbered
  
  The Numbered range causes the time window to occur a fixed number of days (based on the pattern).
  
  The following example will repeat from 6:00 PM to 8:00 PM on Monday and Tuesday until the there are 3 occurrences, which respectively happens on 2024/04/01(Mon), 2024/04/02(Tue) and 2024/04/08(Mon).
```
start: "Mon, 1 Apr 2024 18:00:00 GMT"
end: "Mon, 1 Apr 2024 20:00:00 GMT"
recurrence:
  pattern:
    type: "Weekly"
    interval: 1
    daysOfWeek:
        - Monday
        - Tuesday
  range:
    type: "Numbered"
    numberOfOccurrences: 3
```

TargetingFilter

This filter provides the capability to enable a feature for a target audience. An in-depth explanation of targeting is explained in the targeting section below. The filter parameters include an audience object which describes users, groups, and a default percentage of the user base that should have access to the feature, and an exclusion object for users and groups that should never be targeted. Each group object that is listed in the target audience must also specify what percentage of the group's members should have access. If a user is specified in the users section directly, or if the user is in the included percentage of any of the group rollouts, or if the user falls into the default rollout percentage then that user will have the feature enabled.

feature-management:
  feature-flags:
    target:
      enabled-for:
        -
          name: targetingFilter
          parameters:
            users:
              - Jeff
              - Alicia
            groups:
              -
                name: Ring0
                rolloutPercentage: 100
              -
                name: Ring1
                rolloutPercentage: 100
            defaultRolloutPercentage: 50
            exclusion:
              users:
                - Ross
              groups:
                - Ring2

Targeting

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, 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 TargetingFilter feature filter.

Targeting in an Application

An example web application that uses the targeting feature filter is available in the example project.

To begin using the TargetingFilter in an application it must be added as a @Bean like any other Feature Filter. TargetingFilter relies on another @Bean to be added to the application, ITargetingContextAccessor. The ITargetingContextAccessor allows for defining the current TargetingContext to be used for defining the current user id and groups. An example of this is:

public class TargetingContextAccessor implements ITargetingContextAccessor {

    @Override
    public Mono<TargetingContext> getContextAsync() {
        TargetingContext context = new TargetingContext();
        context.setUserId("Jeff");
        ArrayList<String> groups = new ArrayList<String>();
        groups.add("Ring0");
        context.setGroups(groups);
        return Mono.just(context);
    }

}

Targeting Evaluation Options

Options are available to customize how targeting evaluation is performed across a given TargetingFilter. An optional parameter TargetingEvaluationOptions can be set during TargetingFilter creation.

    @Bean
    public TargetingFilter targetingFilter(ITargetingContextAccessor contextAccessor) {
        return new TargetingFilter(contextAccessor, new TargetingEvaluationOptions().setIgnoreCase(true));
    }

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