Deployment Risk Management & Rollback Strategy Questions
Discuss strategies for managing deployment risk: canary deployments (detect issues in subset), feature flags (quick disable without rollback), smoke testing post-deployment. Understand rollback procedures: full rollback (restore previous version), partial rollback (revert specific services). Know how to handle complications like database schema changes that can't simply rollback.
EasyTechnical
47 practiced
Describe the difference between a full rollback (restore previous version) and a partial rollback (revert a single microservice or undo a single feature). Give examples of when a partial rollback is preferable and explain the risks and coordination needed when services are interdependent.
HardTechnical
49 practiced
You need to apply a backward-incompatible column type change (e.g., varchar -> jsonb) where downgrading is impractical. Propose a detailed step-by-step migration plan that allows you to rollback application code safely at any point, ensures data integrity, and limits customer impact. Include tooling, transactional considerations, and monitoring signals.
HardSystem Design
58 practiced
Create a blueprint for orchestrating partial rollbacks across dozens of dependent microservices without performing a global rollback. Consider dependency graph resolution, service compatibility matrices, coordination engine, and safety constraints to perform a minimal, safe remediation.
HardTechnical
61 practiced
Design an algorithm (pseudocode) to compute a safe canary progression schedule (sequence of traffic percentages) given inputs: target user-impact tolerance, current remaining error budget, observed variance in key metric, and total traffic. The schedule should minimize time-to-rollout while keeping probability of exceeding thresholds below a configurable bound.
MediumSystem Design
74 practiced
Design an automated canary-analysis system (a "canary judge") that integrates with CI/CD to decide whether to promote a canary to full production. Requirements: ingest metrics at scale, compare baseline vs canary over configurable windows, support custom metrics and thresholds, trigger automated rollback or promotion, and expose an API for CI. Provide component diagram and data flow at a high level.
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