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All O`one Data Structure | Super Simple Explanation | Leetcode 432 | codestorywithMIK

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This is the 17th Video of our Playlist "Design Data Struture : Popular Interview Problems" by codestorywithMIK
In this video we will try to solve a good Design DSA Problem : All O`one Data Structure | Super Simple Explanation | Leetcode 432 | 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 : All O`one Data Structure | Super Simple Explanation | Leetcode 432 | codestorywithMIK
Company Tags : Google
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║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣
╠╗║╚╝║║╠╗║╚╣║║║║║═╣
╚═╩══╩═╩═╩═╩╝╚╩═╩═╝
Summary :
The AllOne data structure is designed to track the frequency of keys while efficiently retrieving the key with the highest and lowest counts. It uses a combination of a doubly linked list and a hash map:
Doubly Linked List: The list nodes store a frequency count and a set of keys that share that count. This allows us to maintain the order of counts and traverse between different frequency groups.
Hash Map: The map (keyCountMap) maps each key to its corresponding node in the linked list. This enables quick access and updates to the frequency of any given key.
Increment and Decrement:
When incrementing (inc), the key is moved to the node representing the next higher count. If it doesn't exist, a new node is created.
When decrementing (dec), the key is moved to the node with the next lower count or removed if its count becomes zero.
Efficient Max/Min Retrieval:
This design ensures O(1) complexity for insertion, deletion, and retrieving the max/min keys, making it suitable for scenarios requiring frequent updates and quick lookups.
✨ Timelines✨
00:00 - Introduction
0:26 - Problem Explanation
3:04 - Building Thought Process
8:10 - Why Double Linked List Is Best for this
18:25 - Complete Dry Run with Double Linked List + Map
26:32 - Line By Line Coding
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In this video we will try to solve a good Design DSA Problem : All O`one Data Structure | Super Simple Explanation | Leetcode 432 | 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 : All O`one Data Structure | Super Simple Explanation | Leetcode 432 | codestorywithMIK
Company Tags : Google
╔═╦╗╔╦╗╔═╦═╦╦╦╦╗╔═╗
║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣
╠╗║╚╝║║╠╗║╚╣║║║║║═╣
╚═╩══╩═╩═╩═╩╝╚╩═╩═╝
Summary :
The AllOne data structure is designed to track the frequency of keys while efficiently retrieving the key with the highest and lowest counts. It uses a combination of a doubly linked list and a hash map:
Doubly Linked List: The list nodes store a frequency count and a set of keys that share that count. This allows us to maintain the order of counts and traverse between different frequency groups.
Hash Map: The map (keyCountMap) maps each key to its corresponding node in the linked list. This enables quick access and updates to the frequency of any given key.
Increment and Decrement:
When incrementing (inc), the key is moved to the node representing the next higher count. If it doesn't exist, a new node is created.
When decrementing (dec), the key is moved to the node with the next lower count or removed if its count becomes zero.
Efficient Max/Min Retrieval:
This design ensures O(1) complexity for insertion, deletion, and retrieving the max/min keys, making it suitable for scenarios requiring frequent updates and quick lookups.
✨ Timelines✨
00:00 - Introduction
0:26 - Problem Explanation
3:04 - Building Thought Process
8:10 - Why Double Linked List Is Best for this
18:25 - Complete Dry Run with Double Linked List + Map
26:32 - Line By Line Coding
#coding #helpajobseeker #easyrecipes #leetcode #leetcodequestionandanswers #leetcodesolution #leetcodedailychallenge #leetcodequestions #leetcodechallenge #hindi #india #coding #helpajobseeker #easyrecipes #leetcode #leetcodequestionandanswers #leetcodesolution #leetcodedailychallenge#leetcodequestions #leetcodechallenge #hindi #india #hindiexplanation #hindiexplained #easyexplaination #interview#interviewtips #interviewpreparation #interview_ds_algo #hinglish #github #design #data #google #video #instagram #facebook #leetcode #computerscience #leetcodesolutions #leetcodequestionandanswers #code #learning #dsalgo #dsa #coding #programming #100daysofcode #developers #techjobs #datastructures #algorithms #webdevelopment #softwareengineering #computerscience #pythoncoding #codinglife #coderlife #javascript #datascience #leetcode #leetcodesolutions #leetcodedailychallenge #codinginterview #interviewprep #technicalinterview #interviewtips #interviewquestions #codingchallenges #interviewready #dsa #hindi #india #hindicoding #hindiprogramming #hindiexplanation #hindidevelopers #hinditech #hindilearning #helpajobseeker #jobseekers #jobsearchtips #careergoals #careerdevelopment #jobhunt #jobinterview #github #designthinking #learningtogether #growthmindset #digitalcontent #techcontent #socialmediagrowth #contentcreation #instagramreels #videomarketing #codestorywithmik #codestorywithmick #codestorywithmikc #codestorywitmik #codestorywthmik #codstorywithmik #codestorywihmik #codestorywithmiik #codeistorywithmik #codestorywithmk #codestorywitmick #codestorymik #codestorwithmik
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