Top K Elements

Finding K largest/smallest with min/max heap of size K.

6 min readMastering Data Structures & Algorithms for Software Engineering InterviewsCode

K Largest Elements

Use min-heap of size K. Iterate array: push each element, if size > K, pop smallest. At end, heap contains K largest. Time: O(n log K), Space: O(K).

Gok-largest-with-min-heap.go

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Min-heap keeps K largest seen so far. Smallest of K is at root. New element > root → replace. O(n log K) vs O(n log n) for sorting.

K Smallest Elements

Use max-heap of size K (or min-heap with negated values). Push, pop when size > K. Root is largest of K smallest.

Top K Frequent Elements

Gotop-k-frequent.go

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Count frequencies O(n). Heap of (freq, num) size K. Push all, maintain size K. O(n + m log K) where m = unique elements.

Streaming Top K

For unbounded streams, same min-heap of size K works online. O(log K) per element, O(K) space. Cannot get exact top K without storing all (requires reservoir sampling for approximate).

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