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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

Discovering insights and history

The Insights section of a feature flag shows what variations have been served by the flag over time. This is presented in a graph with several time frames available. By default, it shows the last 60 days:

Figure 9.21 – The Insights section of a feature flag

The chart shows the number of times each variation was evaluated over the time frame. In this example, most of the requests to evaluate the feature flag were on the same day and the targeting was configured to return a 50/50 split for most users, with some requests coming from a specific user who should always receive the true variant.

Overlaying the chart are dotted lines that indicate the times when the configuration of the flag was changed. By being able to see both the variant evaluations and when changes were made, this chart can prove useful when analyzing how the flag is performing and what changes have impacted the variants being served. When hovering over...

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