Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): postgres, analyze, optimizer, statistics
- 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?