A DevOps team must release version 3.4 of a revenue-critical microservice that processes thousands of live transactions each minute. Management wants the new code exercised in production by real users, but also demands that most customers stay on the stable build until the release proves reliable. Which deployment strategy best satisfies these requirements while normal operations continue?
Gradually expose the new version to a small subset of users before wider rollout
Wait until the next scheduled maintenance window before releasing the update
Limit validation to a pre-production lab environment and then switch over immediately
Deploy the new version to all production instances simultaneously
Rolling out a change to a small, controlled subset of systems or users-often called a canary or phased deployment-lets engineers observe real-world metrics, error rates, and user feedback before expanding the release to the remaining environment. Updating every node at once is a high-risk "big-bang" approach, postponing until the next window provides no validation of the new version, and relying solely on lab testing ignores production-specific variables such as real traffic patterns, data volumes, and integrations that can expose hidden defects.
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