A dark launch is a software development technique that involves releasing a new feature to a subset of users before it's officially launched. This approach allows developers to test and refine the feature in a live production environment with real users, but without making it available to everyone.
The term "dark launch" comes from the idea that the feature is "in the dark" or hidden from the majority of users. It's a way to mitigate risk when deploying new features, as it allows potential issues to be identified and addressed before a full-scale rollout.
Feature Development: The new feature is developed and tested in a staging environment.
Dark Launch: The feature is deployed to the production environment but is hidden from most users. Only a select group of users (often internal staff or beta testers) can access the feature.
Testing and Refinement: The feature is used in the real world, and data is collected about its performance. Any issues are fixed, and improvements are made based on user feedback and observed behavior.
Full Launch: Once the feature is working well, it's made available to all users.
Suppose a company is developing a new recommendation algorithm for their e-commerce website. They could dark launch the feature by enabling it for a small percentage of users. These users would start seeing product recommendations powered by the new algorithm, while the rest of the users would continue to see recommendations powered by the old algorithm.
The company could then monitor key metrics (like click-through rate and conversion rate) for the new algorithm, compare them to the old algorithm, and make any necessary adjustments. Once they're confident in the new algorithm's performance, they could roll it out to all users.
Risk Mitigation: Dark launches allow potential issues to be identified and fixed before a feature is rolled out to all users, reducing the risk of a poor user experience or system instability.
Performance Testing: They provide an opportunity to test how a feature performs under real-world conditions, with real users and real data.
User Feedback: They enable early user feedback, which can be used to refine and improve the feature.
Gradual Rollout: They allow for a gradual rollout of new features, which can be easier to manage and less disruptive than a full launch.