Longest Common Prefix: Optimal Solution (Data-Backed Guide)

By DSA Prep Team · March 4, 2026 · 7 min read · Algorithms

Longest Common Prefix is the #15 most asked LeetCode problem globally — a clean string problem that tests methodical thinking and knowledge of multiple valid approaches.

Our analysis of 10,385 verified interview questions across 259 companies shows it appeared 62 times across 24 companies — including Google, Amazon, Meta, Microsoft, Bloomberg, Jane Street, CME Group, Deutsche Bank, HSBC, and Disney.

There are four valid approaches — horizontal scan, vertical scan, sort + compare ends, and Trie. The simplest and most interview-friendly is horizontal scan: use the first string as the initial prefix and shrink it until it matches every other string. Clean, O(n·m), and easy to explain.

The Data: 24 Companies Ask This
Problem Statement
Four Approaches Compared
Code in Python, Java & JavaScript
Complexity Analysis
How to Actually Remember This

The Data: 24 Companies Ask This

Metric	Value
Total Interview Appearances	62 verified mentions
Companies That Ask It	24 companies
Global Rank	#15 out of 10,385 questions
Difficulty	Easy
Core Patterns	String, Trie

📌 Companies That Ask This Problem

Google, Amazon, Meta, Microsoft, Bloomberg, Jane Street, CME Group, Deutsche Bank, HSBC, Disney, Adobe, Apple, Capgemini, Cognizant, Deloitte, DXC Technology, EPAM Systems, HCL, IBM, Infosys, and 4 more.

Problem Statement

LeetCode #14 — Longest Common Prefix

Write a function to find the longest common prefix string amongst an array of strings. If there is no common prefix, return an empty string "".

Examples

Input:  strs = ["flower","flow","flight"]  →  Output: "fl"
Input:  strs = ["dog","racecar","car"]     →  Output: ""
Input:  strs = ["ab","a"]                  →  Output: "a"

Four Approaches Compared

Approach	Idea	Time	Space
Horizontal Scan	Start with strs[0] as prefix, shrink until matches each string	O(n·m)	O(1)
Vertical Scan	Compare column by column across all strings	O(n·m)	O(1)
Sort + Compare Ends	Sort array; compare only first and last string	O(n log n)	O(1)
Trie	Insert all strings, traverse common path	O(n·m)	O(n·m)

💡 Best Interview Answer

Horizontal scan for simplicity and O(1) space. Mention sort + compare as an elegant alternative (sort alphabetically — the longest common prefix of all strings must be a prefix of both the lexicographically smallest and largest string). Know Trie if the interviewer asks about building an autocomplete system.

Code in Python, Java & JavaScript

Python 3 — Horizontal Scan

def longestCommonPrefix(strs: list[str]) -> str:
    prefix = strs[0]

    for s in strs[1:]:
        # Shrink prefix until s starts with it
        while not s.startswith(prefix):
            prefix = prefix[:-1]
            if not prefix:
                return ""

    return prefix

Python 3 — Sort + Compare Ends (Elegant)

def longestCommonPrefix(strs: list[str]) -> str:
    strs.sort()
    first, last = strs[0], strs[-1]
    i = 0
    while i < len(first) and i < len(last) and first[i] == last[i]:
        i += 1
    return first[:i]

Java — Horizontal Scan

class Solution {
    public String longestCommonPrefix(String[] strs) {
        String prefix = strs[0];

        for (int i = 1; i < strs.length; i++) {
            while (!strs[i].startsWith(prefix)) {
                prefix = prefix.substring(0, prefix.length() - 1);
                if (prefix.isEmpty()) return "";
            }
        }
        return prefix;
    }
}

JavaScript — Horizontal Scan

var longestCommonPrefix = function(strs) {
    let prefix = strs[0];

    for (let i = 1; i < strs.length; i++) {
        while (!strs[i].startsWith(prefix)) {
            prefix = prefix.slice(0, -1);
            if (!prefix) return "";
        }
    }
    return prefix;
};

Complexity Analysis

        📊 Complexity (Horizontal Scan)
        Time: O(n·m) — In the worst case, all strings are identical and of length m. Each string comparison takes O(m), done n times. Total O(n·m).
Space: O(1) — Only the prefix string, which shrinks in place. No extra arrays.

    

How to Actually Remember This

        ✅ Spaced Repetition Schedule
        Today: Implement horizontal scan. Then implement sort + compare ends as a follow-up.
Day 3: Implement vertical scan — compare character by character column-wise.
Day 7: Sketch a Trie solution — relevant if interviewer asks about autocomplete or prefix search at scale.
Day 14: Re-implement all three non-Trie approaches from memory in one sitting.

    

Tag on DSAPrep.dev under String and Trie.

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