Denormalization: when might you do it and what’s the trade‑off?

Deep dive

Expanding on the short answer — what usually matters in practice:

• Context (tags): database, denormalization, performance, schema-design
• Data model and access patterns: dominant queries (read/write ratio, sorting, pagination).
• Indexes: when they help vs hurt (write amplification, memory).
• Consistency & transactions: what’s guaranteed and what can bite you.
• Explain the "why", not just the "what" (intuition + consequences).
• Trade-offs: what you gain/lose (time, memory, complexity, risk).
• Edge cases: empty inputs, large inputs, invalid inputs, concurrency.

Examples

A tiny example (query shape):

-- Example: index + query shape
SELECT *
FROM users
WHERE email = '[email protected]'
LIMIT 1;

Common pitfalls

• Too generic: no concrete trade-offs or examples.
• Mixing average-case and worst-case (e.g., complexity).
• Ignoring constraints: memory, concurrency, network/disk costs.

Interview follow-ups

• When would you choose an alternative and why?
• What production issues show up and how do you diagnose them?
• How would you test edge cases?