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

Summary

Running experiments is such a key component of feature management, and LaunchDarkly provides a good level of functionality to make the most of the opportunity that testing in this way presents. By considering the release of code as an experiment with a sense of how key metric(s) should change, teams can be data-driven in their approach to refining their product.

Separating metrics from experiments and flags themselves emphasizes the importance of knowing what it is that is being measured for an experiment. Once that is known, it is easy to determine whether that metric should increase or decrease to conclude that the experiment has been a success. LaunchDarkly leads us into thinking along these lines when creating a metric, and the separation of metrics from a single flag shows how important a metric could be for multiple features.

You should now be able to create metrics and add them to feature flags to understand which variants of a feature perform the best. You should...