What is the time complexity of Search Suggestions System?

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

What data structures are used to solve Search Suggestions System?

This problem uses Array, String, Binary Search, Trie, Sorting, Heap (Priority Queue). Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Search Suggestions System?

Insight 1: Sorting the `products` list lexicographically is crucial for returning the lexicographically smallest suggestions when multiple products share a common prefix. Insight 2: Binary search (or a two-pointer approach that mimics binary search) can efficiently find the range of products that have a common prefix with the current prefix of `searchWord`.

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Search Suggestions System - LeetCode 1268 Solution

Q: Which companies ask LeetCode 1268. Search Suggestions System in interviews?

This problem is frequently asked at Anduril, Atlassian, Citadel, Coursera, Deliveroo and 8 more companies.

Search Suggestions System - Complete Solution Guide

Search Suggestions System is LeetCode problem 1268, 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 are given an array of strings products and a string searchWord . Design a system that suggests at most three product names from products after each character of searchWord is typed. Suggested products should have common prefix with searchWord . If there are more than three products with a common prefix return the three lexicographically minimums products. Return a list of lists of the suggested products after each character of searchWord is typed . Example 1: Input: products = ["mobile","mou

Detailed Explanation

The problem requires us to design a search suggestion system. Given a list of product names (`products`) and a search word (`searchWord`), the system should suggest at most three product names from the `products` list after each character of `searchWord` is typed. The suggested products must have a common prefix with the current prefix of `searchWord`. If there are more than three products with a common prefix, the system should return the three lexicographically smallest products. The output should be a list of lists, where each inner list contains the suggested products after each character of `searchWord` has been typed.

Solution Approach

The solution uses a sorted array of products and a two-pointer approach to find the lexicographically smallest products that share a common prefix with the current prefix of the search word. The main idea is to maintain two pointers, `l` and `r`, representing the left and right boundaries of the search space within the sorted `products` array. For each character typed in the `searchWord`, the `l` and `r` pointers are adjusted to narrow down the range of products that start with the current prefix. After narrowing the search space, the solution extracts at most three products from the range [l, r] and adds them to the result list.

Step-by-Step Algorithm

Step 1: Sort the `products` array lexicographically using `Arrays.sort(products)` or equivalent.
Step 2: Initialize an empty list called `result` to store the suggested products for each prefix of `searchWord`.
Step 3: Initialize two pointers, `l` to 0 and `r` to `products.length - 1`.
Step 4: Iterate through each character `c` of `searchWord` using a `for` loop with index `i`.
Step 5: Within the loop, adjust the left pointer `l` to the right until `products[l]` starts with the prefix of `searchWord` up to index `i`. If `products[l]`'s length is less than or equal to `i`, or `products[l][i]` is not equal to `c`, increment `l`.
Step 6: Similarly, adjust the right pointer `r` to the left until `products[r]` starts with the prefix of `searchWord` up to index `i`. If `products[r]`'s length is less than or equal to `i`, or `products[r][i]` is not equal to `c`, decrement `r`.
Step 7: Create a new list `suggestion` to store the suggested products for the current prefix.
Step 8: Iterate from `l` to `min(l + 2, r)`, adding `products[j]` to the `suggestion` list. This ensures that at most three products are added.
Step 9: Add the `suggestion` list to the `result` list.
Step 10: After the loop finishes, return the `result` list.

Key Insights

Insight 1: Sorting the `products` list lexicographically is crucial for returning the lexicographically smallest suggestions when multiple products share a common prefix.
Insight 2: Binary search (or a two-pointer approach that mimics binary search) can efficiently find the range of products that have a common prefix with the current prefix of `searchWord`.
Insight 3: We need to limit the number of suggestions to at most three, and ensure to handle edge cases like empty prefixes or prefixes with fewer than three matching products.

Complexity Analysis

Time Complexity: O(n log n + m*n)

Space Complexity: O(m)

Topics

This problem involves: Array, String, Binary Search, Trie, Sorting, Heap (Priority Queue).

Companies

Asked at: Anduril, Atlassian, Citadel, Coursera, Deliveroo, Docusign, DoorDash, J.P. Morgan, ServiceNow, Snap, Two Sigma, UBS, Wix.

Search Suggestions System | LeetCode 1268

Medium

Array String Binary Search Trie Sorting Heap (Priority Queue)

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

Solution Code

Available in:

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

class Solution {
    public List<List<String>> suggestedProducts(String[] products, String searchWord) {
        Arrays.sort(products);
        List<List<String>> result = new ArrayList<>();
        int l = 0, r = products.length - 1;

        for (int i = 0; i < searchWord.length(); i++) {
            char c = searchWord.charAt(i);
            while (l <= r && (products[l].length() <= i || products[l].charAt(i) != c)) {
                l++;
            }

            while (l <= r && (products[r].length() <= i || products[r].charAt(i) != c)) {
                r--;
            }

            List<String> suggestion = new ArrayList<>();
            for (int j = l; j <= Math.min(l + 2, r); j++) {
                suggestion.add(products[j]);
            }
            result.add(suggestion);
        }

        return result;
    }
}

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Problem Info

Description

Problem 1268: Search Suggestions System - Detailed explanation and solution approaches.

Topics

Array String Binary Search Trie Sorting Heap (Priority Queue)

Companies

AndurilAtlassianCitadelCourseraDeliverooDocusignDoorDashJ.P. Morgan

Stats

Difficulty:Medium

Languages:4

Companies:13

Problem #:1268

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: Sort the `products` array lexicographically using `Arrays.sort(products)` or equivalent.
2
Step 2: Initialize an empty list called `result` to store the suggested products for each prefix of `searchWord`.
3
Step 3: Initialize two pointers, `l` to 0 and `r` to `products.length - 1`.
4
Step 4: Iterate through each character `c` of `searchWord` using a `for` loop with index `i`.
5
Step 5: Within the loop, adjust the left pointer `l` to the right until `products[l]` starts with the prefix of `searchWord` up to index `i`. If `products[l]`'s length is less than or equal to `i`, or `products[l][i]` is not equal to `c`, increment `l`.
6
Step 6: Similarly, adjust the right pointer `r` to the left until `products[r]` starts with the prefix of `searchWord` up to index `i`. If `products[r]`'s length is less than or equal to `i`, or `products[r][i]` is not equal to `c`, decrement `r`.
7
Step 7: Create a new list `suggestion` to store the suggested products for the current prefix.
8
Step 8: Iterate from `l` to `min(l + 2, r)`, adding `products[j]` to the `suggestion` list. This ensures that at most three products are added.
9
Step 9: Add the `suggestion` list to the `result` list.
10
Step 10: After the loop finishes, return the `result` list.

💡

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 log n + m*n)

✨ Log-linear - Great for sorting algorithms!

💾

Space Complexity

O(m)

📝

Why?

The time complexity is dominated by the initial sorting step O(n log n) and the nested loops which are at worst O(m*k). Space complexity is determined by the space required for result and prefix which is O(m).

📖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

Trie Data Structure

Using a Trie (prefix tree) to store the products allows for efficient prefix-based search. You can traverse the Trie based on the characters of the search word and collect the lexicographically smallest three products at each node.

Time: O(m*k), where m is the length of the `searchWord` and k is the average length of suggested products which are maximum 3.Space: O(N*L), where N is the number of products and L is the average length of the product. This accounts for the space needed to store the Trie.

Trade-offs: Trie is better suited if many searches are required for the same product list. If a single search is required as in this problem, the sorting approach with two pointers is generally preferred because of its simplicity and lower space complexity. Trie requires more memory especially when the products have many distinct prefixes.

✨

Key Insights

1Insight 1: Sorting the `products` list lexicographically is crucial for returning the lexicographically smallest suggestions when multiple products share a common prefix.
2Insight 2: Binary search (or a two-pointer approach that mimics binary search) can efficiently find the range of products that have a common prefix with the current prefix of `searchWord`.
3Insight 3: We need to limit the number of suggestions to at most three, and ensure to handle edge cases like empty prefixes or prefixes with fewer than three matching products.

📚

Key Terms Explained

For Students

Array

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

Binary Search

Halving the search space each step. Like finding a word in a dictionary!

String

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

Sorting

Arranging elements in a specific order (ascending/descending).

Edge Cases

Edge case 1: Empty `products` list. The code handles this correctly because the loops will not execute.
Edge case 2: `searchWord` is empty. The outer loop will not execute, and an empty `result` list will be returned.
Edge case 3: No products match the prefix. The `l` pointer will become greater than `r`, resulting in an empty `suggestion` list.
Edge case 4: Fewer than three products match the prefix. The inner loop will iterate only up to `r`, ensuring that only the available matching products are added to the `suggestion` list.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Failing to sort the `products` list. This will result in incorrect lexicographical ordering of the suggested products.

✗Common mistake 2: Not handling the case where fewer than three products match the prefix. This can lead to `IndexOutOfBoundsException` errors or incorrect suggestions.

✗Common mistake 3: Incorrectly adjusting the `l` and `r` pointers, leading to missing products or incorrect suggestions.

✅

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: Autocomplete feature in search engines or e-commerce websites. When a user types a query, the system suggests relevant search terms or products in real-time.

🏦Real-world scenario 2: Code completion in IDEs. As a developer types code, the IDE suggests possible function names, variable names, or class members based on the current prefix.

💡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: June 9, 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

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

@CodeNinjaJul 13

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

@TechLeadJul 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.

How do I handle string manipulation efficiently?

In languages with immutable strings (Java, Python, C#), use StringBuilder/list to avoid O(n) concatenation cost. Consider using character arrays for in-place modifications. For pattern matching, consider KMP or Rabin-Karp for O(n+m) complexity.

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 "Search Suggestions System"?

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

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