Heap / Priority Queue

A heap keeps the smallest (or largest) element instantly accessible while inserts and removals stay O(log n). Use it for top-k, kth-largest, merging sorted streams, or any time you repeatedly need the current minimum or maximum.

16 LeetCode problems solved with the Heap / Priority Queue pattern. Practice them with spaced repetition so the pattern sticks.

• Find K Pairs with Smallest SumsMEDIUM · O(k log k)
• Find Median from Data StreamHARD · O(log n)
• IPOHARD · O((n + k) log n)
• K Closest Points to OriginMEDIUM · O(n log k)
• Kth Largest Element in a StreamEASY · O(log k)
• Kth Largest Element in an ArrayMEDIUM · O(n log k)
• Last Stone WeightEASY · O(n log n)
• Maximum Subsequence ScoreMEDIUM · O(n log n)
• Meeting Rooms IIMEDIUM · O(n log n)
• Meeting Rooms IIIHARD · O(m log m + m log n)
• Merge k Sorted ListsHARD · O(N log k)
• Minimum Interval to Include Each QueryHARD · O((n + q) log(n + q)) where n = intervals, q = queries
• Single-Threaded CPUMEDIUM · O(n log n)
• Smallest Number in Infinite SetMEDIUM · O(log n)
• Top K Frequent ElementsMEDIUM · O(n log k)
• Total Cost to Hire K WorkersMEDIUM · O((k + candidates) log candidates)