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Feature Management with LaunchDarkly

Feature Management with LaunchDarkly

By : Gillett
4.9 (8)
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Feature Management with LaunchDarkly

Feature Management with LaunchDarkly

4.9 (8)
By: 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)
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1
Section 1: The Basics
5
Section 2:Getting the Most out of Feature Management
11
Section 3: Mastering LaunchDarkly

Chapter 4: Percentage and Ring Rollouts

In the first few chapters of this book, we mentioned rollouts. In this chapter, we will explore what this term means, the value this approach brings to feature management, and how to configure feature flags in LaunchDarkly to perform a feature rollout.

There are two approaches to rolling out a feature in a safe way and validating the value being derived, either as a percentage or as a ring, and in this chapter, we will look at both of these. For these approaches, we need to capture data about the new and existing code we want to release, to allow us to validate that the new functionality is working as expected.

Alongside the more detailed explanations of rollouts will be some example use cases to provide more context for these approaches. Some of these examples are from my own experience, while others will be hypothetical situations that demonstrate the effectiveness of rollouts.

Once the theory and scenarios of rollouts have been covered...

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