What is the time complexity of Unique Email Addresses?

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

What is the space complexity of Unique Email Addresses?

The space complexity is O(n*m).

What data structures are used to solve Unique Email Addresses?

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

What are the key insights for solving Unique Email Addresses?

Insight 1: Separating the local and domain names is crucial for applying the email processing rules. Insight 2: The rules only apply to the local name part of the email address; the domain name is not affected.

Which companies ask LeetCode 929. Unique Email Addresses in interviews?

This problem is frequently asked at Intuit, Twitch, Wix.

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Unique Email Addresses - LeetCode 929 Solution

Unique Email Addresses - Complete Solution Guide

Unique Email Addresses is LeetCode problem 929, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Every valid email consists of a local name and a domain name , separated by the '@' sign. Besides lowercase letters, the email may contain one or more '.' or '+' . For example, in "[email protected]" , "alice" is the local name , and "leetcode.com" is the domain name . If you add periods '.' between some characters in the local name part of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule does not apply to domain names .

Detailed Explanation

The problem requires you to process a list of email addresses and determine the number of unique email addresses that will actually receive mail after applying specific rules. These rules involve handling '.' characters (which are ignored) and '+' characters (where everything after the '+' in the local name is ignored) in the local name part of the email address. The domain name remains unchanged. The goal is to count how many distinct email addresses result after these transformations.

Solution Approach

The solution iterates through each email in the input list, processes the local name according to the given rules (removing '.' and truncating at '+'), and then reconstructs the normalized email address. A set is used to store these normalized email addresses, ensuring that only unique addresses are counted. Finally, the size of the set represents the number of unique email addresses.

Step-by-Step Algorithm

Step 1: Iterate through each email address in the input list.
Step 2: Split each email address into local name and domain name using the '@' character as a delimiter.
Step 3: Process the local name by first finding the index of the '+' character and truncating the local name at that point. If '+' doesn't exist then process the entire local name.
Step 4: Remove all '.' characters from the local name.
Step 5: Concatenate the processed local name, '@', and the domain name to form the normalized email address.
Step 6: Add the normalized email address to a set.
Step 7: After processing all email addresses, return the size of the set, which represents the number of unique email addresses.

Key Insights

Insight 1: Separating the local and domain names is crucial for applying the email processing rules.
Insight 2: The rules only apply to the local name part of the email address; the domain name is not affected.
Insight 3: Using a set is essential to efficiently count the unique email addresses after processing.

Complexity Analysis

Time Complexity: O(n*m)

Space Complexity: O(n*m)

Topics

This problem involves: Array, Hash Table, String.

Companies

Asked at: Intuit, Twitch, Wix.

Unique Email Addresses | LeetCode 929

Easy

Array Hash Table String

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

Solution Code

Available in:

import java.util.HashSet;
import java.util.Set;

class Solution {
    public int numUniqueEmails(String[] emails) {
        Set<String> uniqueEmails = new HashSet<>();
        for (String email : emails) {
            String[] parts = email.split("@");
            String localName = parts[0];
            String domainName = parts[1];
            StringBuilder sb = new StringBuilder();
            for (int i = 0; i < localName.length(); i++) {
                char c = localName.charAt(i);
                if (c == '+') {
                    break;
                }
                if (c != '.') {
                    sb.append(c);
                }
            }
            uniqueEmails.add(sb.toString() + "@" + domainName);
        }
        return uniqueEmails.size();
    }
}

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Problem Info

Description

Problem 929: Unique Email Addresses - Detailed explanation and solution approaches.

Topics

Array Hash Table String

Companies

IntuitTwitchWix

Stats

Difficulty:Easy

Languages:4

Companies:3

Problem #:929

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

7 steps

1
Step 1: Iterate through each email address in the input list.
2
Step 2: Split each email address into local name and domain name using the '@' character as a delimiter.
3
Step 3: Process the local name by first finding the index of the '+' character and truncating the local name at that point. If '+' doesn't exist then process the entire local name.
4
Step 4: Remove all '.' characters from the local name.
5
Step 5: Concatenate the processed local name, '@', and the domain name to form the normalized email address.
6
Step 6: Add the normalized email address to a set.
7
Step 7: After processing all email addresses, return the size of the set, which represents the number of unique email addresses.

💡

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 need to process each email, which involves string manipulations proportional to the email's length. The space complexity arises from storing the potentially unique normalized email addresses in a set.

📖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 Regular Expressions

Regular expressions can be used to remove the '+' and '.' characters from the local name in a more concise way.

Time: O(n*m), where n is the number of emails and m is the average length of the email.Space: O(n*m), similar to the main solution, for storing the unique emails in the set.

Trade-offs: While regular expressions can make the code shorter, they might be slightly less efficient than the iterative approach. The iterative approach is generally easier to understand for beginners.

✨

Key Insights

1Insight 1: Separating the local and domain names is crucial for applying the email processing rules.
2Insight 2: The rules only apply to the local name part of the email address; the domain name is not affected.
3Insight 3: Using a set is essential to efficiently count the unique email addresses after processing.

📚

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: Email address with no '+' character. The code correctly processes the entire local name, removing only '.' characters.
Edge case 2: Email address with multiple '.' characters in the local name. The code removes all '.' characters as expected.
Edge case 3: All email addresses are the same after processing. The set will only contain one entry, and the code returns 1.
Edge case 4: All email addresses are unique after processing. The set will contain all entries, and the code returns the number of emails.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to handle the '+' character correctly, leading to incorrect local name processing.

✗Common mistake 2: Not using a set to store the processed email addresses, resulting in an incorrect count of unique email addresses. A list would count duplicate emails.

✗Common mistake 3: Modifying the original emails list instead of creating new strings for the normalized email addresses. This can lead to unexpected side effects.

✅

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: Email service providers use this type of logic to handle email aliases and spam filtering.

🏦Real-world scenario 2: User account creation and management systems where users might create multiple accounts using variations of their email address.

💡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

Don't rush. Explain your thought process clearly. Easy problems are often about communication.

⚠️ 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: June 30, 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

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 13

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 6

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

@AlgoMasterJun 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.

What should I learn from easy problems?

Easy problems introduce core patterns that appear in harder problems. Master basic operations (iteration, conditionals, simple data structures), recognize common patterns (counting, searching, basic transformations), and practice explaining your thought process clearly.

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 "Unique Email Addresses"?

For this easy-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.

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