What is the time complexity of Design Browser History?

The optimal solution has a time complexity of O(1).

What data structures are used to solve Design Browser History?

This problem uses Array, Linked List, Stack, Design, Doubly-Linked List, Data Stream. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Design Browser History?

Insight 1: A simple array or list is sufficient to store the history of visited URLs. Insight 2: We need to maintain a `current_index` to track the currently visited page and a `last_index` to track the end of the valid history.

Which companies ask LeetCode 1472. Design Browser History in interviews?

This problem is frequently asked at Cisco, DoorDash, Roblox, Snap, Splunk.

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Design Browser History - LeetCode 1472 Solution

Design Browser History - Complete Solution Guide

Design Browser History is LeetCode problem 1472, 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

You have a browser of one tab where you start on the homepage and you can visit another url , get back in the history number of steps or move forward in the history number of steps . Implement the BrowserHistory class: BrowserHistory(string homepage) Initializes the object with the homepage of the browser. void visit(string url) Visits url from the current page. It clears up all the forward history. string back(int steps) Move steps back in history. If you can only return x steps in the history

Detailed Explanation

The problem asks us to simulate a simplified browser history. We need to implement a `BrowserHistory` class that allows us to visit URLs, go back a certain number of steps in history, and go forward a certain number of steps. The browser starts at a given `homepage`. Visiting a new URL clears any forward history. The `back` and `forward` methods should move as many steps as possible within the available history, returning the current URL after the move. The constraints limit the number of calls to each method and the length of the URLs.

Solution Approach

The provided solution uses an array (or List in Java/C++) to store the browsing history. `current_index` points to the currently visited URL, and `last_index` indicates the last valid index in the history array. The `visit` method appends the new URL to the history and updates both `current_index` and `last_index`. If the new `url` is visited after navigating `back`, then all URLs from `current_index + 1` onward are implicitly removed (overwritten), and `last_index` is updated accordingly. The `back` and `forward` methods simply adjust `current_index` within the bounds of the history and return the URL at the updated index.

Step-by-Step Algorithm

Step 1: Initialize the `BrowserHistory` object with the `homepage` by creating a list and adding the `homepage` to it. Set `current_index` and `last_index` to 0.
Step 2: `visit(url)`: Increment `current_index`. If it's beyond the current `last_index` (or list size), append the `url` to the history list. Otherwise, overwrite the existing URL at the `current_index`. Update `last_index` to be equal to `current_index`.
Step 3: `back(steps)`: Move `current_index` back by `steps`, but ensure it doesn't go below 0. Set `current_index = max(0, current_index - steps)`. Return the URL at the new `current_index`.
Step 4: `forward(steps)`: Move `current_index` forward by `steps`, but ensure it doesn't go beyond `last_index`. Set `current_index = min(last_index, current_index + steps)`. Return the URL at the new `current_index`.

Key Insights

Insight 1: A simple array or list is sufficient to store the history of visited URLs.
Insight 2: We need to maintain a `current_index` to track the currently visited page and a `last_index` to track the end of the valid history.
Insight 3: The `back` and `forward` methods need to handle edge cases where the requested number of steps exceeds the available history.

Complexity Analysis

Time Complexity: O(1)

Space Complexity: O(n)

Topics

This problem involves: Array, Linked List, Stack, Design, Doubly-Linked List, Data Stream.

Companies

Asked at: Cisco, DoorDash, Roblox, Snap, Splunk.

Design Browser History | LeetCode 1472

Medium

Array Linked List Stack Design Doubly-Linked List Data Stream

Time: O(1)Space: O(n)

Solution Code

Available in:

import java.util.ArrayList;
import java.util.List;

class BrowserHistory {

    private List<String> history;
    private int current_index;
    private int last_index;

    public BrowserHistory(String homepage) {
        this.history = new ArrayList<>();
        this.history.add(homepage);
        this.current_index = 0;
        this.last_index = 0;
    }
    
    public void visit(String url) {
        this.current_index += 1;
        if (this.current_index == this.history.size()) {
            this.history.add(url);
        } else {
            this.history.set(this.current_index, url);
        }
        this.last_index = this.current_index;
    }
    
    public String back(int steps) {
        this.current_index = Math.max(0, this.current_index - steps);
        return this.history.get(this.current_index);
    }
    
    public String forward(int steps) {
        this.current_index = Math.min(this.last_index, this.current_index + steps);
        return this.history.get(this.current_index);
    }
}

/**
 * Your BrowserHistory object will be instantiated and called as such:
 * BrowserHistory obj = new BrowserHistory(homepage);
 * obj.visit(url);
 * String param_2 = obj.back(steps);
 * String param_3 = obj.forward(steps);
 */

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Problem Info

Description

Problem 1472: Design Browser History - Detailed explanation and solution approaches.

Topics

Array Linked List Stack Design Doubly-Linked List Data Stream

Companies

CiscoDoorDashRobloxSnapSplunk

Stats

Difficulty:Medium

Languages:4

Companies:5

Problem #:1472

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

4 steps

1
Step 1: Initialize the `BrowserHistory` object with the `homepage` by creating a list and adding the `homepage` to it. Set `current_index` and `last_index` to 0.
2
Step 2: `visit(url)`: Increment `current_index`. If it's beyond the current `last_index` (or list size), append the `url` to the history list. Otherwise, overwrite the existing URL at the `current_index`. Update `last_index` to be equal to `current_index`.
3
Step 3: `back(steps)`: Move `current_index` back by `steps`, but ensure it doesn't go below 0. Set `current_index = max(0, current_index - steps)`. Return the URL at the new `current_index`.
4
Step 4: `forward(steps)`: Move `current_index` forward by `steps`, but ensure it doesn't go beyond `last_index`. Set `current_index = min(last_index, current_index + steps)`. Return the URL at the new `current_index`.

💡

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(1)

⚡ Constant time - Super fast! Same speed regardless of input size.

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is O(1) because each method performs a fixed number of operations regardless of the size of the history. The space complexity is O(n) because the history list stores the URLs. The number of URLs stored can grow up to the number of calls to visit()

📖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

Doubly Linked List

A doubly linked list could also be used to implement the browser history. Each node would represent a visited URL, and nodes would have pointers to the previous and next URLs.

Time: O(1) for `visit`, `back`, and `forward` (assuming we maintain a pointer to the current node).Space: O(n), where n is the number of visited URLs.

Trade-offs: Doubly linked list can be useful if you want to remove items in the middle (although not relevant here) or if memory allocation is a concern(although less relevant in modern systems). For this problem, the list based implementation is simpler and usually faster due to caching.

✨

Key Insights

1Insight 1: A simple array or list is sufficient to store the history of visited URLs.
2Insight 2: We need to maintain a `current_index` to track the currently visited page and a `last_index` to track the end of the valid history.
3Insight 3: The `back` and `forward` methods need to handle edge cases where the requested number of steps exceeds the available history.

📚

Key Terms Explained

For Students

Array

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

Stack

LIFO structure - Last In, First Out. Like a stack of plates!

Linked List

Elements connected via pointers. Like a chain where each link knows the next.

Edge Cases

Edge case 1: `back(steps)` when `steps` is larger than the available history. The solution correctly handles this by setting `current_index` to 0, effectively going back to the homepage.
Edge case 2: `forward(steps)` when `steps` is larger than the available forward history. The solution correctly handles this by setting `current_index` to `last_index`, effectively going to the most recently visited page.
Edge case 3: Visiting a new url using visit() after navigating back using back() should clear all the forward history. The code handles this by overwriting the items in the list.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Not handling the edge cases where `steps` is larger than the available history in `back` and `forward` methods, leading to index out of bounds errors.

✗Common mistake 2: Forgetting to update the last_index after calling visit(). This would result in an incorrect forward history.

✗Common mistake 3: Not allocating enough memory when resizing history in C (and forgetting to free old memory!).

✅

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: Web browsers use a similar mechanism to maintain browsing history, allowing users to navigate back and forward.

🏦Real-world scenario 2: Many applications with a history or undo/redo functionality use a similar data structure and approach.

💡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

Consider multiple approaches before coding. Mentioning alternatives shows breadth of knowledge.

⚠️ 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: July 3, 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

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

@InterviewProJul 13

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

@CodeNinjaJul 6

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

Why use a dummy node in linked list problems?

A dummy node simplifies edge cases where the head might change (insertion/deletion at head) or when building a new list. Initialize dummy.next = head, work with dummy.next throughout, and return dummy.next at the end to handle all cases uniformly.

What techniques are commonly tested in medium-difficulty problems?

Medium problems often require combining multiple techniques: two-pointer with hash maps, BFS/DFS with state tracking, dynamic programming with optimization, or divide-and-conquer approaches. They test your ability to recognize patterns and choose the right combination of techniques.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Design Browser History"?

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 linked list techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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