Pattern · Heap (Priority Queue)

Heap (Priority Queue) interview questions

120 heap (priority queue) problems tagged across recent interview reports. Drilled most heavily by doordash, flipkart, and linkedin.

Founder's read

Heap and Priority Queue problems dominate coding interviews at scale. With 120 problems tagged across this pattern, it's one of the most frequently tested algorithmic skills. DoorDash, Flipkart, and LinkedIn hammer candidates on heap variants, from K-way merges to dynamic ordering to greedy scheduling. If you can't instantly recognize when to push to a min-heap or maintain a sorted window, you'll stall on live assessments. StealthCoder reads the problem and solves heap patterns in real time, invisible to the proctor, so you ship working code even when the variant catches you off guard.

Most-asked heap (priority queue) problems

#ProblemDiff# Companies
01Kth Largest Element in an ArrayMEDIUM41
02Find Median from Data StreamHARD31
03Cheapest Flights Within K StopsMEDIUM9
04K Closest Points to OriginMEDIUM9
05Longest Continuous Subarray With Absolute Diff Less Than or Equal to LimitMEDIUM8
06Employee Free TimeHARD7
07Find K Closest ElementsMEDIUM7
08IPOHARD7
09Kth Smallest Element in a Sorted MatrixMEDIUM7
10Car PoolingMEDIUM6
11Find Servers That Handled Most Number of RequestsHARD6
12Furthest Building You Can ReachMEDIUM6
13Longest Happy StringMEDIUM6
14Kth Largest Element in a StreamEASY5
15Design Search Autocomplete SystemHARD4
16Design TwitterMEDIUM4
17Last Stone WeightEASY4
18Find the Safest Path in a GridMEDIUM3
19Closest Binary Search Tree Value IIHARD2
20Construct String With Repeat LimitMEDIUM2
21Course Schedule IIIHARD2
22Design a Food Rating SystemMEDIUM2
23Design Graph With Shortest Path CalculatorHARD2
24Diagonal Traverse IIMEDIUM2
25Divide an Array Into Subarrays With Minimum Cost IIHARD2
26Divide Intervals Into Minimum Number of GroupsMEDIUM2
27Exam RoomMEDIUM2
28Find Subsequence of Length K With the Largest SumEASY2
29Get Biggest Three Rhombus Sums in a GridMEDIUM2
30Largest Number After Digit Swaps by ParityEASY2
31Avoid Flood in The CityMEDIUM1
32Constrained Subsequence SumHARD1
33Construct Target Array With Multiple SumsHARD1
34Cut Off Trees for Golf EventHARD1
35Delivering Boxes from Storage to PortsHARD1
36Design a 3D Binary Matrix with Efficient Layer TrackingMEDIUM1
37Design a File Sharing SystemMEDIUM1
38Design Movie Rental SystemHARD1
39Earliest Second to Mark Indices IIHARD1
40Find Building Where Alice and Bob Can MeetHARD1
41Find Edges in Shortest PathsHARD1
42Find K Pairs with Smallest SumsMEDIUM1
43Find Score of an Array After Marking All ElementsMEDIUM1
44Find the K-Sum of an ArrayHARD1
45Finding MK AverageHARD1
46High FiveEASY1
47Jump Game VIMEDIUM1
48K Highest Ranked Items Within a Price RangeMEDIUM1
49K-th Smallest Prime FractionMEDIUM1
50Lexicographically Minimum String After Removing StarsMEDIUM1

Showing top 50 of 120 heap (priority queue) problems by # companies asking.

The hedge for the live OA

You can't drill every heap (priority queue) variant before the assessment. StealthCoder runs invisibly during screen share and solves whichever variant they throw at you. No browser extension. No detection signature. Made for the engineer who has done the work but might still blank with a webcam pointed at him.

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What this means

Heap problems disguise themselves. They're not always labeled 'use a heap', they hide in phrases like 'top K', 'find the Kth smallest', 'schedule tasks greedily', or 'maintain a running median'. Recognition is the hard part: you need to spot that you're repeatedly finding the min or max across a dynamic set. Common subtypes include K-way merges (merging K sorted lists), dynamic selection (Kth largest in a stream), greedy scheduling (course-schedule-iii, car-pooling), and constrained optimization (cheapest-flights-within-k-stops, constrained-subsequence-sum). The drill order is simple: start with explicit 'top K' problems, then move to disguised greedy problems where the heap isn't mentioned in the problem title. By the time you hit your live OA, you'll recognize the pattern instantly, but if a hard variant lands and your approach cracks, StealthCoder bridges the gap with a correct solution in seconds.

Companies that hire most on heap (priority queue)

The honest play

120 heap (priority queue) problems. You won't drill them all. Pass anyway.

Heap (Priority Queue) is one of the patterns interviews actually filter on. Memorizing every variant in a week is a fantasy. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds, no matter which heap (priority queue) flavor lands in your live OA. Made for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.

Heap (Priority Queue) interview FAQ

How many heap problems should I drill before my interview?+

With 120 problems in this pattern, drilling 30 to 40 across difficulty levels is realistic. Focus on 2 to 3 from each subtype: K-way merges, top-K selection, greedy scheduling, and stream processing. That covers 80% of what you'll see.

Which companies test heap patterns the hardest?+

DoorDash (33 problems), Flipkart (31), and LinkedIn (27) test heap heavily. Apple, Atlassian, and DE Shaw also weight it high. If you're interviewing there, heap is non-negotiable.

How do I recognize a heap problem if it's not labeled as such?+

Look for language like 'K smallest', 'top K', 'find the maximum across a sliding window', 'schedule optimally', or 'maintain sorted order dynamically'. If you're repeatedly picking the extreme value from a changing set, it's a heap problem.

What's the difference between a min-heap and max-heap problem?+

Min-heap finds the smallest element efficiently; max-heap finds the largest. Most languages provide a heap API that defaults to min-heap. For max-heap, negate values or use a custom comparator. Know both instinctively.

Are heap problems harder than other patterns?+

Not intrinsically. The challenge is recognition and implementation speed. Once you spot the pattern, coding is straightforward. That's where drilling matters, muscle memory saves you in a live OA.

Problem and frequency data sourced from public community-maintained interview-report repos. Problems and trademarks © LeetCode.