36 articles
Master two optimal approaches to merge k sorted linked lists: min-heap for O(n log k) time and divide-and-conquer for O(n log k) with lower constant factors. A top-tier interview problem at Amazon, Google, Facebook, Microsoft, and Uber that tests your mastery of heaps and recursive merging.
Master advanced Dijkstra: state augmentation, K-stop limits, two-cost optimisation, k-th shortest paths, and modified relaxation. The interview pattern behind LeetCode 787, 1928, and 1976, asked at Google, Amazon, and Meta.
Master heaps and priority queues for FAANG interviews: 7 core patterns including Top K, Two Heaps, Merge K, scheduling, and streaming median problems.
Solve LeetCode 703 Kth Largest Element in a Stream — a classic Amazon and Meta phone-screen heap problem using a fixed-size min-heap of size K.
Solve LeetCode 1046 Last Stone Weight, a classic Amazon and Google warmup that teaches max-heap simulation by repeatedly smashing the two heaviest stones.
Solve LeetCode 506 Relative Ranks by assigning Olympic-style medals using a max-heap or index-sort, a common Amazon and Google warmup question.
Solve LeetCode 658 Find K Closest Elements using a max-heap of size K or O(log n) binary search on the window boundary, a classic Google and Amazon question.
Solve LeetCode 215 Kth Largest Element using a heap (O(n log k)) or Quickselect (average O(n)). One of the most-asked Meta and Amazon interview problems.
Solve LeetCode 347 Top K Frequent Elements using a min-heap (O(n log k)) or bucket sort (O(n)). One of the most common Meta and Amazon onsite questions.
Solve LeetCode 451 Sort Characters By Frequency with a max-heap or bucket sort. A common Amazon and Meta phone-screen on hash + heap composition.
Solve LeetCode 973 K Closest Points to Origin using a max-heap (O(n log k)) or Quickselect (O(n) average). One of the most-asked Meta and Amazon problems.
Solve LeetCode 621 Task Scheduler with a max-heap + cooldown queue or a closed-form math formula. A favorite Amazon and Meta scheduling interview.
Solve LeetCode 767 Reorganize String with a max-heap by always picking the two most frequent characters. A staple Amazon, Meta, and Google interview problem.
LeetCode 295 (Hard) — a FAANG favorite. Maintain a dynamic median from a live data stream using two heaps: a max-heap for the lower half and a min-heap for the upper half, achieving O(log n) per insert.
LeetCode 480 (Hard) — a Google and Bloomberg favorite. Compute the median of every k-sized window using two heaps with a lazy deletion hash map.
LeetCode 23 (Hard) — the most-asked heap problem at FAANG. Merge k sorted linked lists in O(N log k) using a min-heap that always tracks the smallest active head.
LeetCode 378 (Medium) — a Google and Amazon staple. Solve k-th smallest in a row-and-column-sorted matrix with a min-heap k-way merge or binary search on the value range.
LeetCode 373 (Medium) — find the k smallest pair sums from two sorted arrays in O(k log k) by treating the implicit pair-sum matrix as a sorted matrix and running a min-heap k-way merge.
LeetCode 264 (Medium) — generate the n-th ugly number using a min-heap with a dedupe set or a three-pointer DP merge that runs in pure O(n).
LeetCode 630 (Hard) — a Google scheduling classic. Maximize courses you can finish before deadlines using a greedy max-heap that swaps out the longest course when budget overflows.
LeetCode 1882 Process Tasks Using Servers is a Google and Amazon two-heap scheduling interview classic. Master the priority queue approach that simulates task assignment in O((m+n) log n).
LeetCode 407 Trapping Rain Water II is a Google and Meta hard interview problem. Solve it with min-heap BFS that processes the elevation map inward from the border in O(mn log(mn)).
LeetCode 778 Swim in Rising Water is a Google and Amazon hard problem disguised as Dijkstra. Solve it with a min-heap that minimizes the maximum elevation along any path in O(N^2 log N).
LeetCode 632 Smallest Range Covering Elements from K Lists is a Google and Meta hard interview problem. Solve it with a K-way merge min-heap that maintains a sliding range across K sorted lists in O(N log K).
LeetCode 313 Super Ugly Number is a generalization of Ugly Number II asked in Amazon and Google interviews. Generate the nth number whose only prime factors are in a given list using min-heap or K-pointer DP.
LeetCode 871 Minimum Number of Refueling Stops is an Amazon and Google hard interview problem. Use a max-heap to greedily pick the richest station retroactively in O(N log N).
LeetCode 355 Design Twitter is a Meta and Twitter system design interview classic. Build post, follow, unfollow, and getNewsFeed using a per-user tweet list and a K-way merge max-heap.
LeetCode 1201 Ugly Number III is a Google and Amazon medium that breaks the heap pattern. Solve it with binary search plus inclusion-exclusion in O(log(n*max)) — far faster than naive heap enumeration.
LeetCode 1383 Maximum Performance of a Team is a Google and Amazon hard interview classic. Use a sorted sweep over efficiency with a size-k min-heap of speeds to compute the answer in O(N log N).
LeetCode 1405 Longest Happy String is a Google and Amazon medium interview classic. Greedily build a string with no three consecutive identical chars using a max-heap of remaining counts in O(N log 1).
LeetCode 895 Maximum Frequency Stack is an Amazon, Google, and Meta hard design interview problem. Achieve O(1) push and pop using frequency-bucket stacks — beating the naive max-heap approach.
LeetCode 1675 Minimize Deviation in Array is a Google and Amazon hard interview problem. Reduce it to a one-directional max-heap by doubling odds upfront, then halve the max iteratively in O(N log N log M).
LeetCode 2099 solved with a heap-based top-K selection followed by an index-preserving reconstruction. Tests whether you can decouple selection from ordering — a classic FAANG screening pattern.
Complete master recap of the Heaps and Priority Queues section. Covers 7 core heap patterns, complexity tables, and a full FAANG-style problem index for systematic interview preparation.
Find the minimum CPU intervals to execute all tasks with cooldown n using a greedy formula based on maximum frequency. A key FAANG problem testing greedy reasoning, frequency counting, and optionally heap-based simulation asked at Amazon, Google, and Facebook.
Solve LeetCode 295 Find Median from Data Stream with two heaps (max-heap for low half, min-heap for high half) giving O(log n) addNum and O(1) findMedian. A FAANG streaming classic.