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 to experiment

When working with experiments within LaunchDarkly, we will still be using feature flags, as we explored in Chapter 3, Basics of LaunchDarkly and Feature Management. As we progress through this section, you will want to create some new flags to set up experiments and get to grips with the functionality that LaunchDarkly offers.

The experimentation functionality within LaunchDarkly couples a feature flag with some form of metric that can prove the success or failure of the test. In that regard, we will be using the rollouts from Chapter 4, Percentage and Ring Rollouts, to deliver two or more variants of a feature to the users. In the scenario of running an experiment, one of those variants should be the control (baseline or benchmark), which is the current experience that's available to customers. In any experiment, the existing implementation (including when there is not an existing feature) should be considered the baseline. The new implementation...