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Subdomain Visit Count - Problem

Subdomain Visit Count

Imagine you're building an analytics system for website traffic! When someone visits a website like discuss.leetcode.com, they're actually visiting multiple domains at once:

🌐 discuss.leetcode.com (the full domain)
🌐 leetcode.com (parent domain)
🌐 com (top-level domain)

You're given an array of count-paired domains in the format "count domain". For example, "9001 discuss.leetcode.com" means that discuss.leetcode.com was visited 9001 times.

Your task: Calculate the total visit count for each subdomain (including parent domains) and return them as count-paired strings. The order doesn't matter!

Think of it like a tree structure where visiting a leaf node also counts as visiting all its parent nodes.

Input & Output

example_1.py — Basic Case

$ Input: ["9001 discuss.leetcode.com"]

› Output: ["9001 discuss.leetcode.com", "9001 leetcode.com", "9001 com"]

💡 Note: Visiting discuss.leetcode.com 9001 times means we also visit its parent domains leetcode.com and com 9001 times each.

example_2.py — Multiple Domains

$ Input: ["900 google.mail.com", "50 yahoo.com", "1 intel.mail.com"]

› Output: ["901 mail.com", "50 yahoo.com", "900 google.mail.com", "1 intel.mail.com", "951 com"]

💡 Note: mail.com gets 900+1=901 visits, com gets 900+50+1=951 visits from all the different domains.

example_3.py — Single Level Domain

$ Input: ["1 com"]

› Output: ["1 com"]

💡 Note: A top-level domain like com has no parent domains, so only itself is counted.

Constraints

1 ≤ cpdomains.length ≤ 100
1 ≤ cpdomains[i].length ≤ 100
cpdomains[i] follows either the format "rep_i d1_i.d2_i.d3_i" or "rep_i d1_i.d2_i"
rep_i is an integer in the range [1, 10⁴]
d1_i, d2_i, and d3_i consist of lowercase English letters only

Visualization

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Understanding the Visualization

Parse Input

Extract visit count and full domain name

Generate Subdomains

Split domain and create all parent domain combinations

Update Counters

Add visit count to each subdomain using hash map for O(1) access

Format Output

Convert hash map entries back to count-paired strings

Key Takeaway

🎯 Key Insight: Use hash map for O(1) subdomain lookups, processing each domain only once while updating all its parent domains simultaneously!

Asked in

G Google 45 a Amazon 38 f Meta 22 ⊞ Microsoft 18

The optimal solution uses a hash map to efficiently count subdomain visits in O(n) time. For each count-paired domain, we split it into all possible subdomains and update their counts using O(1) hash map operations, eliminating the need for repeated searching through results.

Common Approaches

Approach	Time	Space	Notes
✓ One-Pass Hash Table (Optimal)	O(n)	O(n)	Use hash map to count all subdomain visits in a single pass
Brute Force (Nested Processing)	O(n²)	O(n)	Process each domain multiple times to count all subdomains

One-Pass Hash Table (Optimal) — Algorithm Steps

Initialize an empty hash map to store subdomain counts
For each count-paired domain, parse count and domain
Split domain by dots to get domain parts
Generate all subdomains from right to left
For each subdomain, add count to hash map (O(1) operation)
Convert hash map entries back to count-paired format

Visualization

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Step-by-Step Walkthrough

Initialize Hash Map

Create empty hash map for O(1) subdomain lookups

Process Each Domain

Split domain and update all subdomain counts instantly

Generate Result

Convert hash map entries to final count-paired format

Code -

solution.c — C

Time & Space Complexity

Time Complexity

⏱️

O(n)

Single pass through input, with O(1) hash map operations for each subdomain

✓ Linear Growth

Space Complexity

O(n)

Hash map stores at most 3n entries (each domain creates up to 3 subdomains)

⚡ Linearithmic Space

42.6K Views

Medium Frequency

~15 min Avg. Time

1.8K Likes

Ln 1, Col 1

Smart Actions

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

One-Pass Hash Table (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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