This topic explains what randomization units are and how to use them in LaunchDarkly Experimentation. An experiment’s randomization unit is the context kind the experiment uses to assign traffic to each of its variations.
When you create an experiment, you will choose a randomization unit that determines both:
Most experiments use the user randomization unit. This means that the experiment will sort contexts that encounter the experiment into different variations by user. You can also choose a different context kind, like device or organization.
To use a particular metric within an experiment, the metric and the experiment must use the same context kind as their randomization unit.
You select a metric’s randomization unit when you create it:
Later, when you create an experiment, select the same randomization unit to make the metric available to the experiment:
Like experiments, many metrics use a context kind of user as their randomization unit, but can also use other kinds depending on what the metric is measuring. To learn more, read Creating and managing metrics.
When you create an experiment, you will select a flag and a flag rule to run the experiment on. The context kind that the flag rule targets should match the randomization unit of your experiment. We recommend running the experiment on a rule that targets a subsection of your contexts rather than the default rule. This helps ensure consistent experiment results.
New context kinds that you create in the LaunchDarkly user interface (UI) are available for experiments by default. You can mark a context kind as available or unavailable for experiments from the Contexts list.
New context kinds automatically created from your SDKs are not marked as available for experiments by default. To make them available for experiments, follow the procedure below.
To make a context kind as available or unavailable for experiments:
If you use multi-contexts, you have options as to which context kind you want to use as a randomization unit for an experiment. Expand the section below to view an example.
Imagine the user Anna and the user Jesse both work for the organization Global Health Services. If they are both in an experiment that randomizes by organization, Anna and Jesse will always be sorted into the same variation in the experiment, because their contexts share the same organization. However, if they are in an experiment that randomizes by user, then they could end up in different variations.
Here is what their multi-contexts would look like, though each SDK sends context data to LaunchDarkly in a slightly different format:
There are two ways you could randomize the multi-contexts in an experiment:
user, Anna could be assigned to one variation, and Jesse could be assigned to the other variation because they have different user keysorganization, Anna and Jesse will both always be assigned to the same variation because they share the same organization key