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 the Experiments dashboard

To begin with, we will look at the Experiments dashboard. This provides an overview of all the feature flags that have experiments set up for the project and the environment being used. This is also where metrics can be managed:

Figure 11.1 – The Experiments dashboard

The Experiments dashboard is comprised of two parts: Overview and Metrics. The Overview screen shows all the flags with experiments and provides three quick filter buttons to refine the list on the dashboard to make management easier. The three filters correspond to the states that an experiment can be in:

  • Recording: This state means that the experiment is active and that data is being recorded. The data is being captured via the metric that was set up for the experiments.
  • Paused: This state means that at some point, the experiment was running with data being captured, but the data collection has now been stopped.
  • Not started: This...