heap

A classic that rewards the two-heap insight. A sorted list gives O(n) inserts; two balanced heaps give O(log n) insert and O(1) median. Here is the implementation and the rebalancing detail people get wrong.

A classic that tests whether you reach past sorting for the right structure. The signal is the heap solution (O(n log k)), the bucket-sort O(n) trick, and how it changes for an unbounded stream. Here is the answer.

Merging k sorted sources is the canonical min-heap problem and a real data-engineering pattern (merging sorted shards/streams). The signal is the heap of k heads giving O(N log k). Here is the answer.

K-th largest has three textbook solutions, and the signal is knowing Quickselect's average O(n) beats sorting's O(n log n), why its worst case is O(n²), and when a heap is actually the better call. Here is the answer.

The minimum number of meeting rooms equals the peak number of meetings running at once. The reframing is the whole interview; two O(n log n) solutions fall out of it, and the tie-handling trips up the careless.