How does the MongoDB aggregation pipeline work?

Deep dive

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

• Context (tags): aggregation, pipeline, mongodb
• 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

Here’s an additional example (building on the short answer):

[
  { "$match": { "status": "paid" } },
  { "$group": { "_id": "$customerId", "total": { "$sum": "$amount" } } },
  { "$sort": { "total": -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?