Backup & Restore Interview Questions

pg_dump is logical backup for individual DBs/tables; base backup + WAL archiving supports PITR and cluster-level backups.

Point-In-Time Recovery uses base backups and WAL to restore the database to a specific timestamp.

Regularly restore backups to a staging environment, verify data, and maintain documented runbooks for different failure modes.

Indexes speed reads but slow writes and consume storage. Analyze query plans, avoid over-indexing, and use composite indexes that match filter and sort columns. Backup & Restore workloads often benefit from covering indexes or partial indexes.

Transactions group operations atomically—either all commit or all roll back. When Backup & Restore involves multi-step updates, use appropriate isolation levels and handle deadlocks. Explain ACID properties if the database is relational.

Schedule regular backups, test restores, and consider point-in-time recovery for critical data. Backup & Restore changes should be recoverable; document RPO/RTO targets and automate backup verification.

Use EXPLAIN/EXPLAIN ANALYZE, review execution plans, check missing indexes, statistics freshness, and lock contention. Profile application queries and avoid N+1 patterns that amplify Backup & Restore load.

Use parameterized queries, least-privilege DB users, encryption at rest and in transit, and audit sensitive access. Never embed credentials in source code; rotate secrets and restrict network access to the database.

Match the database to consistency needs, query patterns, scaling model, and operational expertise. PostgreSQL excels when its data model and features align with Backup & Restore; be honest about trade-offs versus SQL, document stores, or caches.

Use the official PostgreSQL docs for Backup & Restore, language or framework references, and reputable community guides. Bookmark release notes and migration guides when upgrading versions, since Backup & Restore behavior can change between releases.

Copying snippets without understanding why Backup & Restore works leads to fragile code. Beginners often skip error handling, tests, or edge cases. Slow down, trace execution step by step, and validate assumptions with small experiments.

Index columns used in WHERE/JOIN/ORDER BY for frequent queries. Explain write amplification and why blind indexing hurts Backup & Restore.

Look for seq scans vs index scans, bad row estimates, and sort/hash costs. Tie findings to whether Backup & Restore needs a better index or query rewrite.

Indexes are maintained in the same transaction as writes. Long transactions can bloat and delay Backup & Restore cleanup via vacuum.

Parameterized queries, least-privilege roles, and no superuser apps. Audit who can create/drop Backup & Restore objects.

pg_stat_user_indexes for scans vs size. Drop redundant Backup & Restore indexes after confirming with production-like load.

Too many indexes, updating indexed columns frequently, or missing batching. Measure write latency before/after Backup & Restore changes.

Align models with reads/writes, normalize or denormalize intentionally, and plan for growth. Backup & Restore choices should match real query patterns.

Follow team conventions, split concerns into testable units, handle edge cases, and document assumptions. Review similar modules in the codebase, add observability, and ship incrementally with feature flags if Backup & Restore is risky.

Watch for bottlenecks, shared state races, config drift, and unbounded resource usage. Load-test Backup & Restore paths, set limits, and plan horizontal scaling or caching before traffic spikes.