Book Image

Feature Management with LaunchDarkly

By : Michael Gillett
Book Image

Feature Management with LaunchDarkly

By: Michael Gillett

Overview of this book

Over the past few years, DevOps has become the de facto approach for designing, building, and delivering software. Feature management is now extending the DevOps methodology to allow applications to change on demand and run experiments to validate the success of new features. If you want to make feature management happen, LaunchDarkly is the tool for you. This book explains how feature management is key to building modern software systems. Starting with the basics of LaunchDarkly and configuring simple feature flags to turn features on and off, you'll learn how simple functionality can be applied in more powerful ways with percentage-based rollouts, experimentation, and switches. You'll see how feature management can change the way teams work and how large projects, including migrations, are planned. Finally, you'll discover various uses of every part of the tool to gain mastery of LaunchDarkly. This includes tips and tricks for experimentation, identifying groups and segments of users, and investigating and debugging issues with specific users and feature flag evaluations. By the end of the book, you'll have gained a comprehensive understanding of LaunchDarkly, along with knowledge of the adoption of trunk-based development workflows and methods, multi-variant testing, and managing infrastructure changes and migrations.
Table of Contents (18 chapters)
1
Section 1: The Basics
5
Section 2:Getting the Most out of Feature Management
11
Section 3: Mastering LaunchDarkly

Using LaunchDarkly for ring rollouts

To understand how to use ring rollouts, you will want to create a new feature flag, just as we did for the percentage rollouts. Where this approach will differ is within the targeting. To keep this example simple, I will assume we have three rings to roll out a feature to:

  • Alpha testers
  • Beta testers
  • All customers

In this scenario, we will want a feature to be enabled first for our alpha testers. Then, once the feature is validated within that group, either through quantitative or qualitative data, we would serve true for our beta testers and then ultimately make the feature available to all users. To set something like this up, we could use a custom attribute on the User object, where we populate a group attribute with the testing group name for all the customers within a group. In this scenario, the code would only add the custom Group attribute to the User based on some property about the customer's account. There are...