Deep dive
Expanding on the short answer — what usually matters in practice:
- Context (tags): mongo, aggregation, pipeline, performance
- 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 + projection):
// Example: query + projection
const user = await db.collection('users').findOne(
{ email: '[email protected]' },
{ projection: { _id: 0, email: 1, name: 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?