gitGood.dev

Heap / Priority Queue Interview Questions

When you need the largest or smallest element repeatedly but do not need everything sorted, a heap gives you O(log n) per operation instead of a full O(n log n) sort each time. Top-k is the canonical use; the two-heap trick for a streaming median is the one that separates candidates. Remember that a size-k heap solves top-k in O(n log k), which is the answer interviewers are usually fishing for.

Recognise it when you see

"Top/smallest k", a running median, or repeatedly needing the current minimum.

10 problems. Each one runs in an in-browser editor against real test cases, with hints and a worked solution.

Easy2

Last Stone WeightPremium

Easy

Kth Largest Element in a StreamPremium

EasyDesign

Medium5

Kth Largest Element in an ArrayPremium

MediumSortingDivide & Conquer

Top K Frequent ElementsPremium

MediumArrays & Hashing

Minimum Cost to Connect SticksPremium

MediumGreedy

Network Delay TimePremium

MediumGraphsGreedy

Meeting Rooms II (Minimum Rooms)Premium

MediumIntervalsSorting

Hard3

Merge k Sorted ListsPremium

HardLinked ListDivide & Conquer

Find Median from Data StreamPremium

HardDesign

Design TwitterPremium

HardDesignArrays & Hashing

Learn the triggers, not just the templates

The patterns cheat sheet covers all of these in one page - what each one looks like in a problem statement, and the template that follows.

Interview patterns cheat sheet Big-O cheat sheet All coding problems

Heap / Priority Queue Interview Questions

When you need the largest or smallest element repeatedly but do not need everything sorted, a heap gives you O(log n) per operation instead of a full O(n log n) sort each time. Top-k is the canonical use; the two-heap trick for a streaming median is the one that separates candidates. Remember that a size-k heap solves top-k in O(n log k), which is the answer interviewers are usually fishing for.