I knew a min-heap of size k was the right move but I fumbled explaining why you'd keep the smallest of the top-k values at the root.
Clarify the problem requirements first, then propose a min-heap of size K to efficiently maintain the Kth largest element. Explain the add operation and getKthLargest method, analyzing time and space complexity.
Pro tip: Mention that this is a classic streaming problem where a heap is optimal, but also discuss trade-offs with other approaches like sorting or balanced BSTs to show depth. Emphasize that the heap solution handles large streams efficiently.
Ask about constraints: Will K be fixed? How often will getKthLargest be called? What is the expected size of the stream? This shows you think about edge cases and scalability.
Suggest using a min-heap of size K to store the K largest elements seen so far. The root of the heap will be the Kth largest element.
For each new number, if the heap has fewer than K elements, add it. Otherwise, if the number is larger than the heap's root, remove the root and add the new number. The Kth largest is always the heap's root.
Time: O(log K) per add operation, O(1) for getKthLargest. Space: O(K). This is optimal for streaming scenarios.
Mention other approaches like maintaining a sorted list (O(N) insertion) or using a balanced BST (O(log N) but more complex), and explain why the heap is preferred.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.