LeetCode #2456 — MEDIUM

Most Popular Video Creator

Move from brute-force thinking to an efficient approach using array strategy.

Solve on LeetCode
The Problem

Problem Statement

You are given two string arrays creators and ids, and an integer array views, all of length n. The ith video on a platform was created by creators[i], has an id of ids[i], and has views[i] views.

The popularity of a creator is the sum of the number of views on all of the creator's videos. Find the creator with the highest popularity and the id of their most viewed video.

  • If multiple creators have the highest popularity, find all of them.
  • If multiple videos have the highest view count for a creator, find the lexicographically smallest id.

Note: It is possible for different videos to have the same id, meaning that ids do not uniquely identify a video. For example, two videos with the same ID are considered as distinct videos with their own viewcount.

Return a 2D array of strings answer where answer[i] = [creatorsi, idi] means that creatorsi has the highest popularity and idi is the id of their most popular video. The answer can be returned in any order.

Example 1:

Input: creators = ["alice","bob","alice","chris"], ids = ["one","two","three","four"], views = [5,10,5,4]

Output: [["alice","one"],["bob","two"]]

Explanation:

The popularity of alice is 5 + 5 = 10.
The popularity of bob is 10.
The popularity of chris is 4.
alice and bob are the most popular creators.
For bob, the video with the highest view count is "two".
For alice, the videos with the highest view count are "one" and "three". Since "one" is lexicographically smaller than "three", it is included in the answer.

Example 2:

Input: creators = ["alice","alice","alice"], ids = ["a","b","c"], views = [1,2,2]

Output: [["alice","b"]]

Explanation:

The videos with id "b" and "c" have the highest view count.
Since "b" is lexicographically smaller than "c", it is included in the answer.

Constraints:

  • n == creators.length == ids.length == views.length
  • 1 <= n <= 105
  • 1 <= creators[i].length, ids[i].length <= 5
  • creators[i] and ids[i] consist only of lowercase English letters.
  • 0 <= views[i] <= 105

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: You are given two string arrays creators and ids, and an integer array views, all of length n. The ith video on a platform was created by creators[i], has an id of ids[i], and has views[i] views. The popularity of a creator is the sum of the number of views on all of the creator's videos. Find the creator with the highest popularity and the id of their most viewed video. If multiple creators have the highest popularity, find all of them. If multiple videos have the highest view count for a creator, find the lexicographically smallest id. Note: It is possible for different videos to have the same id, meaning that ids do not uniquely identify a video. For example, two videos with the same ID are considered as distinct videos with their own viewcount. Return a 2D array of strings answer where answer[i] = [creatorsi, idi] means that creatorsi has the highest popularity and idi is the id of

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array · Hash Map

Example 1

["alice","bob","alice","chris"]
["one","two","three","four"]
[5,10,5,4]

Example 2

["alice","alice","alice"]
["a","b","c"]
[1,2,2]

Related Problems

  • Design Video Sharing Platform (design-video-sharing-platform)
  • Design a Food Rating System (design-a-food-rating-system)
Step 02

Core Insight

What unlocks the optimal approach

  • Use a hash table to store and categorize videos based on their creator.
  • For each creator, iterate through all their videos and use three variables to keep track of their popularity, their most popular video, and the id of their most popular video.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2456: Most Popular Video Creator
class Solution {
    public List<List<String>> mostPopularCreator(String[] creators, String[] ids, int[] views) {
        int n = ids.length;
        Map<String, Long> cnt = new HashMap<>(n);
        Map<String, Integer> d = new HashMap<>(n);
        for (int k = 0; k < n; ++k) {
            String c = creators[k], i = ids[k];
            long v = views[k];
            cnt.merge(c, v, Long::sum);
            if (!d.containsKey(c) || views[d.get(c)] < v
                || (views[d.get(c)] == v && ids[d.get(c)].compareTo(i) > 0)) {
                d.put(c, k);
            }
        }
        long mx = 0;
        for (long x : cnt.values()) {
            mx = Math.max(mx, x);
        }
        List<List<String>> ans = new ArrayList<>();
        for (var e : cnt.entrySet()) {
            if (e.getValue() == mx) {
                String c = e.getKey();
                ans.add(List.of(c, ids[d.get(c)]));
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n)
Space
O(n)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.