Interview › Databases & Caching
How do you plan capacity and storage autoscaling for a growing RDS database?
Databases & Caching · Advanced level
Answer
For RDS capacity, I plan storage, IOPS, throughput, CPU, memory, connections, WAL/binlog growth, backup/restore time, and data growth. Storage autoscaling is useful but does not replace forecasting and schema/data lifecycle work.
Technical explanation
RDS storage autoscaling can prevent out-of-space incidents but does not shrink storage.
Growth affects backup time, restore time, vacuum, indexes, migrations, and query plans.
Capacity planning should forecast storage, IOPS, throughput, CPU, memory, connections, and logs.
Hands-on example
Forecast:
Free storage = 500 GB
Growth = 20 GB/day
Days remaining = 25
Threshold = 45 days, so act now.
SQL:
SELECT relname, pg_size_pretty(pg_total_relation_size(oid)) FROM pg_class WHERE relkind = 'r' ORDER BY pg_total_relation_size(oid) DESC LIMIT 20;
Final Revision Checklist
Answer the first section in 60 to 90 seconds.
Use hands-on examples to prove production experience.
Mention monitoring, rollback, and validation for every risky change.
Tie caching answers to consistency and failure modes.
Tie RDS answers to HA, backups, performance, and safe operations.
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More Databases & Caching interview questions
- What is Amazon RDS, and what does it manage for you versus self-managed databases?
- What database engines does RDS support?
- What is the difference between RDS and Aurora?
- What is Multi-AZ in RDS, and how does automatic failover work?
- How long does an RDS Multi-AZ failover typically take, and what triggers it?
- What is the difference between Multi-AZ and a read replica?
- When would you use a read replica, and can it become a standalone database?
- Can a read replica be in a different region, and why would you do that?