Phased release to limit update risk
risk-managed rollout knowledge.
enable phased release so an approved update reaches a growing percentage of automatic-update users over seven days; pause if metrics spike.
WHAT THIS TESTS The interviewer evaluates whether you can mitigate the blast radius of a risky update operationally rather than just hoping QA caught everything.
A GOOD ANSWER COVERS Phased release is the App Store Connect feature. After your update is approved by review, you opt into phased release for automatic updates; App Store Connect then delivers the new version to a gradually increasing share of users who have automatic updates enabled, ramping over about seven days from a small percentage to all of them. Crucially you retain control: you can pause the phased release to halt further rollout while you investigate a spike in crashes or support tickets, then resume, or you can release to one hundred percent immediately if confident. This caps the number of users hit by a regression in the early days, buying time to ship a fix.
COMMON WRONG ANSWERS Thinking phased release also throttles new installs or users who manually update; those people still get the latest version right away, and the product page shows it. Believing the rollout cannot be paused or stopped once underway. Confusing it with TestFlight or with server-side feature flags, which are complementary but different tools.
LIKELY FOLLOW-UPS How does phased release interact with users who manually update? When would server-side feature flags be a better risk control? How do you monitor metrics to decide whether to pause?
ONE CONCRETE EXAMPLE You ship a major redesign and enable phased release. On day one only a small fraction of automatic-update users receive it. Your monitoring shows the crash-free rate dropping in that cohort, so you pause the phased release from App Store Connect, preventing the rest of the base from receiving the bad build. You ship a fixed build through an expedited path, and only then resume or restart the rollout, having limited the regression to a small slice of users instead of everyone at once.
Read the original → developer.apple.com
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