Binary TreesBinary Trees36
  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Inorder Traversal of a Binary Tree using Recursion
  4. 4Inorder Traversal of a Binary Tree using Iteration
  5. 5Postorder Traversal of a Binary Tree Using Recursion
  6. 6Postorder Traversal of a Binary Tree using Iteration
  7. 7Level Order Traversal of a Binary Tree using Recursion
  8. 8Level Order Traversal of a Binary Tree using Iteration
  9. 9Reverse Level Order Traversal of a Binary Tree using Iteration
  10. 10Reverse Level Order Traversal of a Binary Tree using Recursion
  11. 11Find Height of a Binary Tree
  12. 12Find Diameter of a Binary Tree
  13. 13Find Mirror of a Binary Tree
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree
GraphsGraphs46
  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

Longest Common Prefix in Array of Strings Optimal Vertical Scanning

Problem Statement

Given an array of strings, your task is to find the longest common prefix shared among all the strings.

  • The longest common prefix is the starting portion of each string that is identical across all strings.
  • If no common prefix exists, return an empty string "".
  • If the input array is empty, return "" as well.

This problem is commonly asked in coding interviews to test your understanding of string manipulation and iteration logic.

Examples

Input Strings Longest Common Prefix Description
["flower", "flow", "flight"] "fl" "fl" is the longest starting part common in all three strings
["dog", "racecar", "car"] "" No common prefix exists
["interview", "internet", "internal"] "inte" All strings share "inte" as a common starting sequence
["a"] "a" Only one string — the prefix is the entire string
[""] "" Single empty string, no prefix
[] "" Empty input array — return empty string
["apple", "apple", "apple"] "apple" All strings are exactly the same
["test", "testing", "tester", "testify"] "test" "test" is the longest starting match across all strings
["abc", ""] "" One string is empty, so no common prefix

Visualization Player

Solution

The goal is to find the longest portion at the beginning of all strings that is identical. This is called the longest common prefix.

Imagine writing all the words in a vertical column and comparing them letter by letter, from top to bottom. We go through the characters of the first string one at a time and check whether the same character appears in the same position in all other strings.

Let’s explore the different cases you might encounter:

  • Normal case: If the strings have a common start — like ["flower", "flow", "flight"] — we start comparing character by character. At position 0, all start with 'f'. At position 1, all have 'l'. At position 2, 'flower' and 'flow' have 'o', but 'flight' has 'i'. So the common prefix ends just before the mismatch: "fl".
  • No match at all: If the first characters don’t even match — like in ["dog", "racecar", "car"] — there is no common prefix, and we return "".
  • Single string: If there’s only one string, like ["a"], that string itself is the longest common prefix.
  • Some string is empty: If even one of the strings is empty — like ["abc", ""] — there can’t be a common prefix. Return "".
  • Empty array: If the input array has no strings at all — return "" right away.
  • All strings are identical: In cases like ["apple", "apple", "apple"], all characters will match and the whole string is the common prefix.

This method is known as vertical scanning because it checks each character vertically across all strings at a particular index. It stops as soon as it finds a mismatch, making it efficient and easy to understand.

Edge cases like empty arrays or strings are simple to handle with early checks. This way, we avoid unnecessary comparisons and ensure correctness in all scenarios.

Algorithm Steps

  1. Check if the input array is empty. If so, return "".
  2. Loop through each character index i of the first string.
  3. For each string in the array, check if the character at position i matches that in the first string.
  4. If a mismatch is found or if a string is shorter than i, return the prefix found up to that point.
  5. If no mismatch is found, the entire first string is the common prefix.

Code

Java
Python
JavaScript
C
C++
C#
Kotlin
Swift
Go
Php
public class LongestCommonPrefix {
  public static String longestCommonPrefix(String[] strs) {
    if (strs == null || strs.length == 0) return "";

    for (int i = 0; i < strs[0].length(); i++) {
      char ch = strs[0].charAt(i); // Take character from first string
      for (int j = 1; j < strs.length; j++) {
        // If i is out of bounds or character doesn't match, return prefix
        if (i >= strs[j].length() || strs[j].charAt(i) != ch) {
          return strs[0].substring(0, i);
        }
      }
    }
    return strs[0];
  }

  public static void main(String[] args) {
    String[] input = {"flower", "flow", "flight"};
    System.out.println("Longest Common Prefix: " + longestCommonPrefix(input));
  }
}

Time Complexity

CaseTime ComplexityExplanation
Best CaseO(n)When all strings are identical, only the length of one string is scanned.
Average CaseO(n × m)Where n is the number of strings and m is the length of the shortest string.
Worst CaseO(n × m)In the worst case, all strings are compared fully until a mismatch is found at the end.

Space Complexity

O(1)

Explanation: Only a few variables are used for iteration and prefix tracking. No extra memory required.