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

Learning about experimentation

The Experimentation section of a feature flag was covered in detail in Chapter 5, Experimentation, so it is best to read that chapter to understand how best to use this functionality, along with Metrics. However, there are a couple of additional pieces of functionality that were not covered in that chapter that we will cover next.

When working with the experimentation functionality, it is possible that testing can distort the results of the test. Usually, the only data that should be collected and analyzed is that of the percentage or ring rollout of a flag to customers. When testers or stakeholders are validating or signing off a feature, their usage of an experiment should not impact the data. Normally, the sign-off will be performed before an experiment is presented to any customers. However, testing might continue in production (even once live) to ensure that all the variations of an experiment work. In that case, specific users or targeting rules...