Heap / Priority Queuehard70 minFrontend Engineer

Autocomplete Real-Time Top-K Prioritizer

Frontend Engineer signal: heap + top-k in a search autocomplete context. This is a ProdMatch-owned frontend engineer drill, framed as a March 2026 Lenskart Search UX simulation, not a copied platform question.

Company context

Lenskart · Search UX

Freshness

March 2026

Product surface

search autocomplete

ProdMatch interview simulation based on product-team patterns; not a claim of a real company question.

Question

Your Search UX team needs a live top-k view for queries. Each update changes an item's score. Return the current top k item IDs after each update, ordered by score desc then ID asc.

Input

Initial scores, k, and update stream [id, delta].

Output

Top-k IDs after every update.

Constraints

1 <= items <= 200000
1 <= updates <= 200000
Scores can be negative.

Concepts

render scheduling
state graphs
virtualization
heap
top-k
streaming rank

scores: [5,1,3], k=2, update [1,+5] -> [1,0]

Approach

Try framing your own approach first. The 30 seconds you think before peeking is where learning happens.

Clean Solution

Reveal the approach first.

How well did you understand?

Your rating tunes when this problem shows up again.

Common Mistakes

Use a stable tie-breaker for equal scores.
Do not sort the full stream when k is small.

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