PostgreSQLmedium

Window functions: what problem do they solve? Give a quick example.

Answer

Window functions compute values over a “window” of rows while keeping each row in the result (unlike GROUP BY which collapses rows). They’re great for rankings, running totals, and “top N per group”.

SELECT user_id,
       created_at,
       ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC) AS rn
FROM logins;

Advanced answer

Deep dive

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

Context (tags): postgres, sql, window-functions, analytics
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):

SELECT user_id,
       created_at,
       ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY created_at DESC) AS rn
FROM logins;

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?