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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteA rollback plan is useful only if your team can recognize when a release is failing, decide who should act, and restore a known-good state safely. Before deployment, define what failure looks like, which signals will reveal it, how long to observe them, and who owns the decision. Then test the recovery procedure—including how it handles data and state.
Define failure before the release
There is no universal error-rate or latency threshold that should trigger every rollback. A threshold is meaningful only in relation to the service, the change, and its expected effect on users. Agree with workload and business owners on the release’s success criteria and the conditions that count as failure before traffic reaches the new version.
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Make each condition observable and actionable. For example, specify the affected component or release cohort, the signal to watch, the threshold, the observation window, and the person or system responsible for acting. Include customer or usage indicators when they are relevant; infrastructure health alone may not show whether a feature is working for people.
Record the known-good version or artifact that the team can return to. Also decide in advance whether a detected problem calls for pausing the rollout, rolling back, disabling a feature, or fixing forward. AWS recommends using monitoring to verify deployment success or failure and to inform rollback decisions in its guidance on planning for unsuccessful changes.
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Choose signals that expose the release’s effect
Monitor both technical health and the outcomes the change is meant to affect. A service-wide dashboard can look healthy while a new version harms a small group of users: healthy traffic from the unchanged version may dilute the signal. For a staged rollout, compare the changed cohort with a control so responders can distinguish release-related regressions from broader variation.
Google SRE defines canarying as a “partial and time-limited deployment of a change in a service and its evaluation.” A canary limits initial exposure and gives a team a way to compare versions, but it does not define failure criteria or provide a recovery procedure by itself. See Google SRE’s canarying guidance for the approach and its monitoring considerations.
Match the measurement interval to the rollout. A short canary can be obscured by metrics averaged over a longer period; Google SRE recommends using monitoring intervals no longer than the canary duration. Decide the evaluation window before rollout, long enough to see the relevant effects but not so long that a failing change continues to reach users without a decision.
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Make the response clear and safe
Name the person or role authorized to halt the rollout, trigger a rollback, disable a feature, or choose a fix-forward path. Give responders access to the change information they need, including what changed and which release is active. Microsoft’s safe deployment recommendations advise halting a rollout when an issue is detected, then investigating its severity.
Rollback is not automatically the safest response. The decision depends on severity, cause, user impact, whether the prior version is still safe, and whether data and dependencies can be restored consistently. AWS guidance allows a documented fix-forward path in some circumstances. Where conditions are measurable and the recovery action is safe, automate the decision and response; retain a human path for ambiguous or high-impact situations. AWS describes integrating tests, success criteria, monitoring, and automated rollback in its guidance on automated testing and rollback.
Account for data and state
Reverting code or configuration does not necessarily undo data written by the new version. For schema changes, migrations, or other stateful releases, plan data handling separately: determine whether new writes can be reversed, replicated, dual-written, or require a restore or fail-forward process.
Migration cutovers need explicit checkpoints, a named decision-maker, and a plan for transactions accepted after the change. Simply directing traffic back to the old system may leave it stale if it did not receive those transactions. AWS’s cutover guidance covers checkpoints, data handling, and rollback ownership.
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Document and exercise the recovery procedure before relying on it. Confirm that the required permissions, dependencies, artifacts, and decision-makers are available, and define how responders will verify that service has recovered. Include the steps for pausing exposure and validating the result, not just the command or switch that initiates a rollback.
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After a deployment or rollback, review how long the outage or degradation lasted and update the plan based on what happened. AWS recommends documenting and testing recovery plans and measuring outage duration in its unsuccessful-change guidance. For workload-specific failure conditions and rollback planning, Microsoft’s cloud-native planning guidance also recommends defining conditions and testing the recovery approach.
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