Longest Common Suffix Queries | Simple TRIE | Clean Code | Leetcode 3093 | codestorywithMIK
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This is the 4th Video of our Playlist "TRIE : Popular Interview Problems".
In this video we will try to solve an extremely good problem :
Longest Common Suffix Queries | Simple TRIE | Clean Code | Leetcode 3093 | codestorywithMIK
I will explain the intuition so easily that you will never forget and start seeing this as cakewalk EASYYY.
We will do live coding after explanation and see if we are able to pass all the test cases.
Also, please note that my Github solution link below contains both C++ as well as JAVA code.
Problem Name : Longest Common Suffix Queries | Simple TRIE | Clean Code | Leetcode 3093 | codestorywithMIK
Company Tags : will update soon
My solutions on Github(C++ & JAVA) : https://github.com/MAZHARMIK/Interview_DS_Algo/blob/master/Trie/Longest%20Common%20Suffix%20Queries.cpp
Leetcode Link : https://leetcode.com/problems/longest-common-suffix-queries/description/
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Approach Summary :
The provided solution utilizes a trie data structure to efficiently store and retrieve words from the `wordsContainer` array. The `insertTrie` method constructs a trie by iteratively inserting characters of each word from the `wordsContainer` array into the trie. Each node in the trie represents a character, and the children of each node represent subsequent characters in the word.
The `search` method traverses the trie based on characters from a given word to find the index of the longest word in the `wordsContainer` array that matches the prefix of the given word. It updates the result index as it traverses the trie, ensuring it holds the index of the longest word found so far.
In the `stringIndices` method, the trie is constructed by calling `insertTrie` for each word in `wordsContainer`. Then, it performs a search for each word in the `wordsQuery` array, utilizing the constructed trie to find the appropriate indices. The results are stored in an array and returned.
The trie data structure efficiently stores words, facilitating quick retrieval and comparison of prefixes. By traversing the trie, the solution identifies the longest word that matches the prefix of each word in the `wordsQuery` array. The solution achieves a time complexity of O(m + n), where `m` is the total number of characters in `wordsContainer` and `n` is the average length of words in `wordsQuery`. The space complexity is influenced primarily by the trie structure and the input and output arrays, resulting in a space complexity of O(m), where `m` is the total number of characters in `wordsContainer`.
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✨ Timelines✨
00:00 - Introduction
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