Heap & Priority Queue — the shape behind dozens of interview problems.

Heap & Priority Queue in Python

import heapq
def k_largest(nums: list[int], k: int) -> list[int]:
    heap: list[int] = []
    for v in nums:
        if len(heap) < k:
            heapq.heappush(heap, v)
        elif v > heap[0]:
            heapq.heapreplace(heap, v)
    return heap

See language-specific tabs at /languages/python/patterns and the SQL variant at /languages/sql/patterns.

Related patterns

Frequently asked questions

What is the heap & priority queue pattern?

Heap & Priority Queue is a data structure that returns the min or max in o(log n) per operation. Insert/pop O(log n); peek O(1); heapify O(n).

When should I use heap & priority queue?

Use it when: Top-K or k-th largest queries. Merge-K sorted streams. Scheduling the earliest-available resource.

What are the most common heap & priority queue bugs?

Forgetting Python's heapq is a min-heap (negate for max). Pushing the full array then popping k — wastes O(n log n) vs O(n log k). Tie-breaking with non-comparable payloads — wrap in a tuple with a counter.

What problems use the heap & priority queue pattern?

Representative scenarios: Leaderboard Live-Rank — top-k scores as events stream. Barista Rotation — next free barista among k. Multi-Feed Merger — k-way merge of sorted feeds.

Is heap & priority queue on the Blind 75?

Yes — Heap & Priority Queue is one of the core patterns behind the Blind 75 list. See /blind-75 for the complete mapping.