DSA topic · Stage 3

Heaps interview preparation

Maintain access to an evolving minimum or maximum without fully sorting everything.

DSA prep Practice 6 questions

Concept

Heaps suit top-k selection, streaming priorities, and merging ordered sources.

Complexity lens

Know build-heap, push, and pop costs and compare a size-k heap with full sorting.

Common mistakes

Choosing min-heap versus max-heap backwards
Forgetting stale entries
Sorting after using a heap

Recognition

Decide from constraints, not labels.

These clues narrow the model. They do not replace an invariant or complexity check.

Recognition clues

Only the next minimum/maximum or best k items matter
Candidates arrive over time
Several sorted sources must advance incrementally

Implementation options

Min-heap for next-smallest priority
Bounded min-heap for largest k values
Heap entries with stable tie-breakers or lazy stale-entry removal

Interview behavior

Say why the heap direction matches the retained set
Bound heap growth when solving top-k
Include comparator and stale-entry behavior in the explanation

Patterns that operate on this topic

Heap SelectionFor bounded top-k, the heap contains exactly the best k processed candidates and its root is the weakest retained one.Frequency MapThe table contains exactly the information from the processed prefix that a future element may need.

Representative problems

6 current Heaps questions.

Exact active distribution: 1 easy, 4 medium, 1 hard. External records contain metadata and original guidance only.

External catalogUnverified
Kth Largest Element in an Array
Compare a bounded heap with sorting and selection algorithms.
Arrays Heaps Heap Selection
MediumFree / publicSolve problem
External catalogUnverified
Top K Frequent Elements
Separate counting from selecting the highest-frequency keys.
Arrays Hash Maps Heaps Frequency Map Heap Selection
MediumFree / publicSolve problem
Engineering FoundryOriginalVerified
Merge Service Queues
Merge ordered event streams without loading every event at once.
Heaps Stacks & Queues Heap Selection
Read original prompt
Several services each expose events ordered by timestamp. Produce one globally ordered stream while holding at most one pending event from each service. When timestamps tie, order by service identifier.
MediumFree / publicSolve problem
External catalogUnverified
Merge k Sorted Lists
Keep only the next candidate from each ordered list in the heap.
Linked Lists Heaps Heap Selection
HardFree / publicSolve problem
External catalogUnverified
Kth Largest Element in a Stream
Shows why retaining only K candidates beats sorting every update.
Heaps Heap Selection
EasyFree / publicSolve problem
External catalogUnverified
K Closest Points to Origin
Tests whether heap direction matches the candidates being discarded.
Heaps Heap Selection
MediumFree / publicSolve problem

Review state

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Review prompts

Did I choose min versus max backwards?
Could heap construction start in O(n)?
Was a full sort simpler and acceptable?

Prerequisites and next step

Graph Thinking

Model relationships explicitly and reason about reachability, order, components, grids, and weighted paths.

Open the roadmap

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