Variation reassignment

This topic explains how LaunchDarkly reassigns contexts to flag variations when you change or increase traffic in an experiment.

Overview

This topic explains how LaunchDarkly reassigns contexts to flag variations when you change or increase traffic in an experiment.

When you make certain edits to an experiment, such as adding additional metrics or increasing traffic, LaunchDarkly creates a new experiment iteration. By default, LaunchDarkly reshuffles experiment traffic when you start a new iteration to help keep traffic balanced between variations.

If you want to keep contexts assigned to their original variation, you can disable variation reassignment for your experiment. You may want to do this when it’s important to minimize disruption to the end-user experience, such as when different variations display different versions of navigation menus or other significant user interface (UI) elements.

Disable variation reassignment

To disable variation reassignment for a new or existing experiment:

1. Navigate to the experiment’s Design tab.
2. In the “Audience allocation” section, click Advanced.
3. Check the Prevent variation reassignment when traffic increases checkbox.

Checking the Prevent variation reassignment when traffic increases checkbox prevents variation reassignment only if you make changes to an experiment using the experiment’s Edit design button.

If you use the Stop button instead, LaunchDarkly will always reshuffle traffic into new variations when you start a new iteration, whether or not this box is checked.

Example of an experiment with variation reassignment disabled

Here is an example of running an experiment with variation reassignment disabled: you add an experiment to a flag with three variations: A, B, and C. You roll out the three variations to 6% of contexts, while the remaining 94% receives the control, variation A. The control traffic is not part of the experiment nor its analysis.

Here is a visualization of the starting traffic allocation, with the control group on the right:

Next, you ramp up your experiment to 30% of traffic. This creates a new iteration of the experiment. In the new iteration, the 6% that were receiving variations A, B, and C, continue to receive those variations, but are no longer included in the experiment nor its analysis. New traffic is used for the 30% allocated to the experiment.

Here is an example of the modified allocation, with the control group on the right and the original experiment audience on the left: