A city permitting database on Amazon Aurora PostgreSQL sees sudden IOPS spikes when inspectors sync field photos and plan reviews after lunch. Storage grows unpredictably as new permit years open. Which design best meets the spiky I/O and growing footprint without constant manual resize tickets?
Select an answer to reveal the explanation.
Short Explanation
Think of Aurora’s storage like a municipal warehouse that adds floor space as boxes arrive—you do not call a contractor every Friday to pour more concrete. Size the IOPS/performance path for the after-lunch surge, and let storage grow with the permit archive.
Full Explanation
Amazon Aurora automatically expands cluster storage as data grows, which fits municipal databases whose footprint expands with each permit year. For spiky civic workloads, architects should plan capacity units and IOPS (or Aurora’s performance-related settings) against the service-level objective rather than relying on fixed legacy volume growth tickets. Caching every blob in Redis or using instance store ignores durability and relational needs. Vertical instance upgrades alone do not solve storage growth or sustained I/O planning.