Dark Launches vs Rolling Deployment
Developers should use dark launches when deploying high-risk features, conducting A/B tests to validate user behavior, or implementing gradual rollouts to minimize impact on users meets developers should use rolling deployment in production environments where high availability is critical, such as for web applications, apis, or microservices that cannot afford extended outages. Here's our take.
Dark Launches
Developers should use dark launches when deploying high-risk features, conducting A/B tests to validate user behavior, or implementing gradual rollouts to minimize impact on users
Dark Launches
Nice PickDevelopers should use dark launches when deploying high-risk features, conducting A/B tests to validate user behavior, or implementing gradual rollouts to minimize impact on users
Pros
- +It's particularly valuable in continuous delivery pipelines to ensure stability and performance before full release, and in large-scale applications where failures could have significant consequences
- +Related to: feature-flags, continuous-deployment
Cons
- -Specific tradeoffs depend on your use case
Rolling Deployment
Developers should use rolling deployment in production environments where high availability is critical, such as for web applications, APIs, or microservices that cannot afford extended outages
Pros
- +It is particularly useful in cloud-based or containerized setups (e
- +Related to: continuous-deployment, blue-green-deployment
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Dark Launches if: You want it's particularly valuable in continuous delivery pipelines to ensure stability and performance before full release, and in large-scale applications where failures could have significant consequences and can live with specific tradeoffs depend on your use case.
Use Rolling Deployment if: You prioritize it is particularly useful in cloud-based or containerized setups (e over what Dark Launches offers.
Developers should use dark launches when deploying high-risk features, conducting A/B tests to validate user behavior, or implementing gradual rollouts to minimize impact on users
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