More information on how to use Flipt is noted in the Getting Started documentation.
Flags are the basic unit in the Flipt ecosystem. Flags represent experiments or features that you want to be able to enable or disable for users of your applications.
For example, a flag named
new-contact-page, could be used to determine whether
or not a given user sees the latest version of a contact us page that you are
working on when they visit your homepage.
Flags can be used as simple on/off toggles or with variants and rules to support more elaborate use cases.
Variants are options for flags. For example, if you have a flag
that determines which main colors your users see when they log in to your
application, then possible variants could be include
Variants can also have JSON attachments as of v1.6.1.
Segments allow you to split your user base or audience up into predefined slices. This is a powerful feature that enables targeting groups to determine if a flag or variant applies to them.
An example segment could be
Segments are global across the Flipt application so they can be used in multiple rules.
When configuring a segment you can choose a
Match Type of either:
Constraints allow you to determine which segment a given entity is a part of.
For example, for a user to fall into the above
new-users segment, you may want
to check their
All constraints have a property, type, operator and optionally a value.
Rules allow you to tie your flags, variants and segments together by specifying which segments are targeted by which variants.
Rules can be as simple as
IF IN segment THEN RETURN variant_a or they can be
more rich by using distribution logic to rollout features on a percent basis.
Continuing our previous example, we may want to return the flag variant
for all entities in the
new-users segment. This would be configured like so:
Rules are evaluated in order per their rank from 1-N. The first rule that matches wins. Once created, rules can be re-ordered to change how they are evaluated.
Distributions allow you to rollout different variants of your flag to percentages of your user base based on your rules.
Let’s say that instead of always showing the
blue variant to your
segment, you want to show blue to 30% of
red to 10%, and
to the remaining 60%. You would accomplish this using rules with distributions:
This is an extremely powerful feature of Flipt that can help you seamlessly deploy new features of your applications to your users while also limiting reach of potential bugs.
Evaluation is the process of sending requests to the Flipt server to process and determine if that request matches any of your segments, and if so which variant to return.
In the above example involving colors, evaluation is where you send information
about your current user to determine if they are a
new-user, and which color
green) that they should see for their main color scheme.
Evaluation works by uniquely identifying each thing that you want to compare
against your segments and flags. We call this an
entity in the Flipt
ecosystem. More often than not this will be a user, but we didn’t want to make
any assumptions on how your application works, which is why
entity was chosen.
what you want to test against in your application
For Flipt to successfully determine which bucket your entities fall into, it
must have a way to uniquely identify them. This is the
entityId and it is a
simple string. It’s up to you what that
It could be a:
Anything that is unique enough for your application and it’s requirements.
The final piece of the puzzle is context. Context allows Flipt to determine which segment your entity falls into by comparing it to all of the possible constraints that you defined.
metadata associated with your entity, used to determine which if any segments that entity is a member of
Examples of context could include:
Think of these as pieces of information that are usually not unique, but that can be used to split your entities into your segments.
You can include as much or as little context for each entity as you want, however the more context that you provide, the more likely it is that a entity will match one of your segments.
context is a simple map of key-value pairs where the key is the
property to match against all constraints, and the value is what is compared.