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DSA Course: Interview Patterns and Problem Solving
Module 12: Heap & Priority Queue
Best Time to Buy and Sell Stock: Greedy Pattern
Maximum Subarray: Kadane Pattern
Move Zeroes: Two pointers Pattern
Contains Duplicate: Set Pattern
Valid Anagram: Frequency map Pattern
Longest Substring Without Repeating Characters: Sliding window Pattern
Valid Palindrome: Two pointers Pattern
Longest Palindromic Substring: Expand around center Pattern
Group Anagrams: Hash key Pattern
Binary Search: Classic search Pattern
Search Insert Position: Lower bound Pattern
First Bad Version: Predicate search Pattern
Search in Rotated Sorted Array: Rotated search Pattern
Find Minimum in Rotated Sorted Array: Rotated minimum Pattern
Valid Parentheses: Stack matching Pattern
Min Stack: Auxiliary stack Pattern
Daily Temperatures: Monotonic stack Pattern
Next Greater Element I: Monotonic stack Pattern
Evaluate Reverse Polish Notation: Stack evaluation Pattern
Reverse Linked List: Pointer reversal Pattern
Merge Two Sorted Lists: Dummy node Pattern
Linked List Cycle: Fast and slow pointers Pattern
Middle of the Linked List: Fast and slow pointers Pattern
Remove Nth Node From End: Two pointers Pattern
Binary Tree Traversals: DFS recursion Pattern
Maximum Depth of Binary Tree: Height recursion Pattern
Binary Tree Level Order Traversal: BFS queue Pattern
Validate Binary Search Tree: Range bounds Pattern
Lowest Common Ancestor: Recursive split Pattern
Connected Components: Adjacency DFS Pattern
Number of Islands: Grid DFS Pattern
Flood Fill: Boundary DFS Pattern
Clone Graph: Hash Map DFS Pattern
Course Schedule: Topological Sort Pattern
Union Find Components: Disjoint Set Pattern
Shortest Path in Unweighted Graph: BFS Distance Pattern
Climbing Stairs: Fibonacci DP Pattern
House Robber: Pick or Skip DP Pattern
Coin Change: Minimum Coins DP Pattern
Longest Increasing Subsequence: Binary Search DP Pattern
Longest Common Subsequence: 2D DP Pattern
0/1 Knapsack: Capacity DP Pattern
Longest Consecutive Sequence: Hash Set Pattern
Subarray Sum Equals K: Prefix Sum Hashmap Pattern
First Unique Character: Frequency Map Pattern
Find Duplicates: Frequency Map Pattern
Ransom Note: Character Availability Pattern
Sort Colors: Dutch National Flag Pattern
Next Permutation: Pivot and Suffix Reversal Pattern
Merge Intervals: Sort and Sweep Pattern
Find First and Last Position: Boundary Binary Search Pattern
Search a 2D Matrix: Flattened Binary Search Pattern
Subsets: Pick or Skip Recursion Pattern
Generate Parentheses: Valid State Backtracking Pattern
Combination Sum: Reuse Choice Backtracking Pattern
N-Queens: Constraint Backtracking Pattern
Word Search: Grid Backtracking Pattern
Kth Largest Element: Size-K Min-Heap Pattern
Top K Frequent Elements: Frequency Heap Pattern
Merge K Sorted Lists: Min-Heap Multiway Merge Pattern
Median Finder: Two Heaps Pattern
Task Scheduler: Greedy Max-Heap Pattern
Jump Game: Farthest Reach Greedy Pattern
Gas Station: Greedy Reset Pattern
Non-overlapping Intervals: Earliest End Greedy Pattern
Minimum Arrows to Burst Balloons: Interval End Greedy Pattern
Partition Labels: Last Occurrence Greedy Pattern
Single Number: XOR Cancellation Pattern
Power of Two: n and n-1 Pattern
Number of 1 Bits: Brian Kernighan Pattern
Single Number III: Rightmost Set Bit Pattern
XOR From 1 to N: Modulo Cycle Pattern
Prime Check: Square Root Trial Division Pattern
Sieve of Eratosthenes: Prime Marking Pattern
GCD: Euclidean Remainder Pattern
Binary Exponentiation: Fast Power Pattern
Modular Inverse: Extended Euclid Pattern
Implement Trie: Prefix Tree Pattern
Longest Common Prefix: Single Branch Trie Pattern
LRU Cache: Hash Map Plus Recency List Pattern
Segment Tree: Range Sum Query Pattern
Fenwick Tree: Binary Indexed Prefix Sum Pattern
CONTENTS

Merge K Sorted Lists: Min-Heap Multiway Merge Pattern

Merge many sorted linked lists by always taking the smallest current head.

DSA Course: Interview Patterns and Problem Solving
Module 12: Heap & Priority Queue
dsa
heap-priority-queue
+1
May 29, 2026
23
A

Learning Outcome

After this lesson, you should be able to use a heap to choose the next smallest node among k sorted streams.

Problem Statement

Given k sorted linked lists, merge them into one sorted linked list.

InputOutputWhy
lists = [[1,4,5],[1,3,4],[2,6]][1,1,2,3,4,4,5,6]The smallest available list head is repeatedly appended to the output.

Brute Force Approach

Merge lists one by one. The growing merged list may be scanned repeatedly.

Optimized Approach

Push the first node of every non-empty list into a min-heap. Pop the smallest node, append it, and push its next node.

Exact Pseudocode

heap = first node of each non-empty list
dummy = new node
tail = dummy
while heap is not empty:
  node = pop smallest
  tail.next = node
  tail = tail.next
  if node.next exists:
    push node.next
return dummy.next

Reference Code

import heapq

class Solution:
    def mergeKLists(self, lists):
        heap = []
        for i, node in enumerate(lists):
            if node:
                heapq.heappush(heap, (node.val, i, node))

        dummy = ListNode(0)
        tail = dummy
        while heap:
            _, i, node = heapq.heappop(heap)
            tail.next = node
            tail = tail.next
            if node.next:
                heapq.heappush(heap, (node.next.val, i, node.next))

        return dummy.next

Sample Dry Run

StepStateResult
Initializeheap has heads 1,1,2Smallest heads are ready
Pop 1Append node and push its next 4Output starts [1]
Pop 1 then 2Push following nodes as neededOutput [1,1,2]
FinishHeap becomes emptyAll nodes are merged

Complexity

MeasureValueReason
TimeO(n log k)Each of n nodes is pushed and popped from a heap of at most k nodes.
SpaceO(k)The heap stores at most one current node per list.

Edge Cases

  • Empty input lists should be skipped.
  • All lists empty should return null.
  • Duplicate values should remain in sorted order.

Interview Checklist

  • Push only non-null list heads.
  • Use a dummy node for easy output building.
  • After popping a node, push its next node if it exists.

FAQs

Why does the heap stay size k?

It stores at most one active node from each list at a time.

Why use a dummy node?

It avoids special handling for the first appended node.

What is the core pattern?

Min-heap multiway merge.

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Merge K Sorted Lists - Min-Heap Multiway Merge Pattern Practice Quiz
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Lesson 3 of 5 in Module 12: Heap & Priority Queue
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Top K Frequent Elements: Frequency Heap Pattern
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Median Finder: Two Heaps Pattern
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