What is the time complexity of Subdomain Visit Count?

The optimal solution has a time complexity of O(n*m).

What is the space complexity of Subdomain Visit Count?

The space complexity is O(n*m).

What data structures are used to solve Subdomain Visit Count?

This problem uses Array, Hash Table, String, Counting. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Subdomain Visit Count?

Insight 1: Using a hash table (dictionary/map) to store the counts for each subdomain is efficient. Insight 2: Iterating through the domain parts from right to left allows easy construction of the subdomains.

Which companies ask LeetCode 811. Subdomain Visit Count in interviews?

This problem is frequently asked at Karat, Peloton, Roblox, Wix.

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Subdomain Visit Count - LeetCode 811 Solution

Subdomain Visit Count - Complete Solution Guide

Subdomain Visit Count is LeetCode problem 811, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

A website domain "discuss.leetcode.com" consists of various subdomains. At the top level, we have "com" , at the next level, we have "leetcode.com" and at the lowest level, "discuss.leetcode.com" . When we visit a domain like "discuss.leetcode.com" , we will also visit the parent domains "leetcode.com" and "com" implicitly. A count-paired domain is a domain that has one of the two formats "rep d1.d2.d3" or "rep d1.d2" where rep is the number of visits to the domain and d1.d2.d3 is the domain its

Detailed Explanation

The problem requires us to process an array of strings, where each string represents a domain and the number of visits to that domain. The goal is to calculate the total visits for each subdomain (e.g., 'discuss.leetcode.com', 'leetcode.com', 'com') and return the results as an array of strings in the format 'count domain'. A key concept is that a visit to a full domain also counts as a visit to all its parent subdomains.

Solution Approach

The solution uses a hash table (dictionary/map) to store the counts for each subdomain. The input array is iterated. For each domain, the count is extracted, and the domain is split into parts. Then, the code iterates through the domain parts, building the subdomain and updating the count in the hash table. Finally, the hash table is converted into the desired output format of an array of strings.

Step-by-Step Algorithm

Step 1: Initialize a hash table (domain_counts) to store the counts for each domain.
Step 2: Iterate through the input array of count-paired domains (cpdomains).
Step 3: For each cpdomain, split it into the count and the full domain.
Step 4: Convert the count from string to integer.
Step 5: Split the full domain into parts based on the '.' delimiter.
Step 6: Iterate through the domain parts from right to left. Construct a subdomain by joining the domain parts.
Step 7: Update the count for the subdomain in the domain_counts hash table.
Step 8: After processing all cpdomains, iterate through the domain_counts hash table.
Step 9: For each entry in the hash table, format the output string as 'count domain'.
Step 10: Return the array of formatted strings.

Key Insights

Insight 1: Using a hash table (dictionary/map) to store the counts for each subdomain is efficient.
Insight 2: Iterating through the domain parts from right to left allows easy construction of the subdomains.
Insight 3: String manipulation and splitting domains based on '.' are crucial for extracting subdomains.

Complexity Analysis

Time Complexity: O(n*m)

Space Complexity: O(n*m)

Topics

This problem involves: Array, Hash Table, String, Counting.

Companies

Asked at: Karat, Peloton, Roblox, Wix.

Subdomain Visit Count | LeetCode 811

Medium

Array Hash Table String Counting

Time: O(n*m)Space: O(n*m)

Solution Code

Available in:

import java.util.List;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;

class Solution {
    public List<String> subdomainVisits(String[] cpdomains) {
        Map<String, Integer> domainCounts = new HashMap<>();

        for (String cpdomain : cpdomains) {
            String[] parts = cpdomain.split(" ");
            int count = Integer.parseInt(parts[0]);
            String fullDomain = parts[1];

            String[] domainParts = fullDomain.split("\\.");

            for (int i = 0; i < domainParts.length; i++) {
                StringBuilder subdomainBuilder = new StringBuilder();
                for (int j = i; j < domainParts.length; j++) {
                    subdomainBuilder.append(domainParts[j]);
                    if (j < domainParts.length - 1) {
                        subdomainBuilder.append(".");
                    }
                }
                String subdomain = subdomainBuilder.toString();
                domainCounts.put(subdomain, domainCounts.getOrDefault(subdomain, 0) + count);
            }
        }

        List<String> result = new ArrayList<>();
        for (Map.Entry<String, Integer> entry : domainCounts.entrySet()) {
            result.add(entry.getValue() + " " + entry.getKey());
        }

        return result;
    }
}

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Problem Info

Description

Problem 811: Subdomain Visit Count - Detailed explanation and solution approaches.

Topics

Array Hash Table String Counting

Companies

KaratPelotonRobloxWix

Stats

Difficulty:Medium

Languages:4

Companies:4

Problem #:811

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

10 steps

1
Step 1: Initialize a hash table (domain_counts) to store the counts for each domain.
2
Step 2: Iterate through the input array of count-paired domains (cpdomains).
3
Step 3: For each cpdomain, split it into the count and the full domain.
4
Step 4: Convert the count from string to integer.
5
Step 5: Split the full domain into parts based on the '.' delimiter.
6
Step 6: Iterate through the domain parts from right to left. Construct a subdomain by joining the domain parts.
7
Step 7: Update the count for the subdomain in the domain_counts hash table.
8
Step 8: After processing all cpdomains, iterate through the domain_counts hash table.
9
Step 9: For each entry in the hash table, format the output string as 'count domain'.
10
Step 10: Return the array of formatted strings.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(n*m)

💾

Space Complexity

O(n*m)

📝

Why?

The time complexity is determined by the nested loops: iterating through cpdomains and iterating through domain parts. The space complexity is determined by the number of unique subdomains that can be generated and stored in the hash table.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using Trie data structure

A Trie (prefix tree) could be used to store the subdomains. Each node would store the count for that subdomain. While potentially more memory-efficient for very large datasets with overlapping subdomains, the implementation is more complex.

Time: O(n*m), where n is the number of cpdomains and m is the length of the longest domain. Similar to the hash table solution.Space: O(n*m) in the worst case, but potentially less if many domains share common prefixes.

Trade-offs: The Trie approach might be beneficial when there are many overlapping subdomains and memory usage is a primary concern. However, the hash table solution is generally simpler to implement and understand.

✨

Key Insights

1Insight 1: Using a hash table (dictionary/map) to store the counts for each subdomain is efficient.
2Insight 2: Iterating through the domain parts from right to left allows easy construction of the subdomains.
3Insight 3: String manipulation and splitting domains based on '.' are crucial for extracting subdomains.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

String

A sequence of characters. Like a word or sentence in programming!

Edge Cases

Edge case 1: Empty input array. The code handles this gracefully because the loops simply don't execute, and an empty list is returned.
Edge case 2: Domains with only one part (e.g., "5 org"). The algorithm correctly processes these, treating "org" as both the full domain and the subdomain.
Edge case 3: Very long domain names. The solution should work, as long as there are enough resources available to the system.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Not handling the case where the input count is a string and needs to be converted to an integer.

✗Common mistake 2: Modifying the input string directly using strtok (in C) without making a copy, which can lead to unexpected behavior.

✗Common mistake 3: Forgetting to split the domain into subdomains correctly based on the '.' delimiter. Incorrectly handling the subdomains will result in the wrong counts.

✗Common mistake 4: Not handling memory allocation and deallocation properly, particularly in C, leading to memory leaks.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Analyzing website traffic data to understand which domains and subdomains are most popular.

🏦Real-world scenario 2: Implementing a domain name suggestion system where the frequency of different subdomains can be used to rank suggestions.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

If stuck, try working through a small example by hand. The pattern often reveals itself.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 31, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

The key insight here is recognizing the pattern. Once you see it, the solution becomes straightforward.

@AlgoMasterJul 13

I was asked this exact problem at my Google interview. The interviewer wanted me to optimize space complexity.

@InterviewProJul 6

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJun 29

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

How do I choose between a hash map and a hash set?

Use a hash set when you only need to track presence/absence of elements (like detecting duplicates). Use a hash map when you need to associate values with keys (like counting frequencies or storing indices). Hash sets use less memory since they only store keys.

How much time should I spend on a medium problem in interviews?

In a 45-minute interview, aim for: 5-10 minutes understanding and clarifying, 5-10 minutes discussing approach, 20-25 minutes coding, and 5-10 minutes testing. If stuck after 15 minutes of thinking, verbalize your thought process and ask for hints.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Subdomain Visit Count"?

For this medium-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

🤖 Need help with this problem?