Define feature flag treatments and targeting

Define feature flag treatments and targeting

Afer you create a feature flag, you can define treatments, dynamic configurations, and targeting rules.

This allows you to deploy features for internal testing and beta releases. You can also progressively roll out features to your production environment.

Feature flag treatments represent feature variations, with a given variation returned from the feature flag for a given user ID, as defined by the flag's targeting rules.

This article provides an overview of creating a feature flag definition for a given environment, and links to articles with more specific configuration details.

Create a feature flag

On the Create a feature flag pane, create a feature flag by filling in metadata useful to your team.

Initiate your feature flag definition for a given environment

Initiating your feature flag definition for a given environment will create an initial set of treatments and a targeting rule that you can modify.

If a feature flag definition is not initiated for an environment, the following page appears:

To initiate your feature flag definition for an environment, do one of the following:

• Start defining treatments and targeting rules by clicking the Initiate environment button.
• Fast track by copying this flag's definitions from another environment. In the Copy from field, select the environment from which you would like to copy this flag's definitions and click the Copy button.

Once the feature flag definition exists for a given environment, the feature flag definition tab will show treatments and targeting rules similar to the following:

You will be able to modify the feature flag treatments, dynamic configurations, and targeting rules before saving. This allows you to configure your feature flag for a given environment.

The next sections guide you through configuring your feature flag definition.

Setting up treatments

Treatment names are strings that are returned when you evaluate the feature flag in your code.

In the Treatments area, do the following:

1. Enter the name of each treatment and optionally enter a description.
2. Optionally, click Add treatment to add additional treatments.
3. Select the default treatment. This treatment will be served to everyone if the feature flag is killed. This treatment is also served to all traffic not exposed to the feature flag (if you Limit exposure when setting up targeting).

Setting up dynamic configuration

In the Dynamic configuration area, optionally define custom JSON or Key-value pairs that you can retrieve in your code. This allows you to add dynamic configurations to your feature variations.

Setting up targeting

Targeting defines how your feature flag will be evaluated for a given user ID (user key). You can add inidividual user IDs to your targeting rules.

Targeting rules have a limit of 500 user IDs, so we suggest creating a segment if you need to target a large number of user IDs.

To set up targeting, you can use the Individual targets area and/or the Targeting rules area. Both are described below.

Individual targets

In the Individual targets area, you can:

Click the Add new individual target button to assign a selected treatment to individual users or segments (e.g., a QA team, internal team, or a limited beta), as shown below.

Targeting rules

In the Targeting rules area, you can:

• Optionally click the Limit exposure button to limit the traffic exposed to your attribute based targeting rules. For example, you can limit the customers exposed to your experiment to the percentage you choose. Users who are not exposed get the default treatment.

• Click Add attribute based targeting rules to assign individual users or segments to a selected treatment or percentage distribution.

  Within attribute based targeting rules, you can:

  • Target with custom attributes to target specific subsets of your users based on a specific attribute (e.g., location or last-login date).

  • Target with dependencies to target users based on the treatment they received from another flag. You can use flag dependencies to create mutually exclusive experiments.

  • Optionally select Distribute treatments as follows and define a percentage distribution to randomly distribute users and segments between your feature flag treatments (variations) based on the percentages you decide. This is also called a percentage rollout, as shown below.

  

Setting the alert baseline treatment

In the Set alert baseline treatment area, select a baseline treatment that will be used for your alert policies.

Rules evaluation order

The rules are evaluated in the following order:

• The first evaluation is against the individual target section. User IDs and segments listed in this section receive the specified treatment.
• The second evaluation is against the traffic allocation. If the limit exposure is set to 100%, we continue to the targeting rules section. If the limit exposure less than 100%, we allocate a percentage of your traffic to the default treatment selected in the user interface or into the targeting rules and default rule.
• The third evaluation is against the targeting rules. The conditions are structured as layered if/else statements and are meant to be human readable. They are evaluated in order and when a condition is met, the evaluation stops.
• The fourth evaluation is against the default rule. The remaining traffic is allocated based on the distribution set in the default rule.