MongoDBmedium

Aggregation pipeline — what is it and what is it used for?

Answer

It’s a sequence of stages (`$match`, `$group`, `$project`, ...) that transforms documents and can compute aggregates. Use it for reporting, grouping, filtering, and shaping data on the server side.

db.orders.aggregate([
  { $match: { status: "PAID" } },
  { $group: { _id: "$customerId", total: { $sum: "$amount" } } }
])

Advanced answer

Deep dive

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

Context (tags): aggregation, pipeline, group
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):

db.orders.aggregate([
  { $match: { status: "PAID" } },
  { $group: { _id: "$customerId", total: { $sum: "$amount" } } }
])

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?