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#Lru Cache Reel by @next.tech12 - LRU Cache Explained 🔥 The O(1) Trick Every FAANG Interviewer Loves LRU Cache is one of the most asked system design + coding interview problems. Many
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@next.tech12
LRU Cache Explained 🔥 The O(1) Trick Every FAANG Interviewer Loves LRU Cache is one of the most asked system design + coding interview problems. Many developers try solving it using arrays or queues… but that won't give O(1) operations. The real trick interviewers expect is combining: • HashMap for fast lookup • Doubly Linked List for ordering recently used items This allows both get() and put() operations in O(1) time. If you're preparing for product-based companies or FAANG interviews, this concept is a must-know. Save this reel for your DSA revision and follow for more coding interview tricks 🚀 Comment “CACHE” if you want more system design and DSA interview questions. #leetcode #codinginterview #datastructures #algorithms #python
#Lru Cache Reel by @engineeringdigest.in (verified account) - 🚀 Master LRU Cache in Java in just 60 seconds! Ever wondered how we implement caching in just 20 lines of code? 🤯 In this quick guide, we'll learn:
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@engineeringdigest.in
🚀 Master LRU Cache in Java in just 60 seconds! Ever wondered how we implement caching in just 20 lines of code? 🤯 In this quick guide, we'll learn: Why extending LinkedHashMap is genius What makes access order so special How automatic entry removal works Save this for your next system design interview! 💡 Follow @theengineeringdigest for more Java tips and DSA concepts explained simply! ✨ Drop a ❤️ if you learned something new! #JavaProgramming #CodingInterview #DSA #SystemDesign #CodingLife #LeetCode #Programming #Developer #SoftwareEngineering #CodingTutorial #JavaDeveloper #ProgrammingTutorial #CodeNewbie #TechInterview #JavaTutorial #CodingCommunity #CodingBoot #developerlife #javaprogramming #javatutorial
#Lru Cache Reel by @iamsaumyaawasthi (verified account) - Designing a Least Recently Used (LRU) cache is a common interview question (Asked in Citi Bank) An LRU cache evicts the least recently used items fir
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@iamsaumyaawasthi
Designing a Least Recently Used (LRU) cache is a common interview question (Asked in Citi Bank) An LRU cache evicts the least recently used items first when it reaches its capacity. Here’s how we can approach this problem: Main Concepts: Cache Operations: The cache should support two primary operations: get(key): Return the value of the key if it exists in the cache, otherwise return -1. put(key, value): Insert or update the value of the key. If the cache reaches its capacity, it should invalidate the least recently used item. Data Structures: HashMap: For O(1) access to cache items by key. Doubly Linked List: To keep track of the usage order of cache items. The most recently used items are moved to the front, and the least recently used items are at the end. Class Structure LRU Cache Class This class will contain the core logic of the LRU cache. Attributes: capacity: The maximum number of items the cache can hold. map: A HashMap that maps keys to nodes in the doubly linked list. head and tail: Nodes to represent the boundaries of the doubly linked list. Methods: get(int key): Retrieves an item from the cache. put(int key, int value): Adds or updates an item in the cache. Helper methods for managing the doubly linked list: addNode(Node node): Adds a new node right after the head. removeNode(Node node): Removes an existing node from the list. moveToHead(Node node): Moves an existing node to the head. popTail(): Removes the node at the tail and returns it. Node Class This class represents each node in the doubly linked list. Attributes: key: The key of the cache item. value: The value of the cache item. prev: Pointer to the previous node. next: Pointer to the next node. #Coding #Programming #TechInterview #SoftwareEngineering #LRUCache #DataStructures #Algorithms #TechTips #CodingInterview #Developer #TechCareers #HashMap #DoublyLinkedList #LearnToCode #CitiBank #TechJobs #ProgrammerLife #coding #codingpatterns #algorithms #programming #interviewprep #softwareengineer #TechCareers #WomenInTech #TechCommunity #CodeDaily #java#roadmap #interview #coding #learning
#Lru Cache Reel by @madeline.m.zhang - More DS&A, today talking about LRU caches! ~~~~~~~~~~~~~~~~ 💻 Follow @madeline.m.zhang for coding memes & insights ~~~~~~~~~~~~~~~~ #learntocode
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@madeline.m.zhang
More DS&A, today talking about LRU caches! ~~~~~~~~~~~~~~~~ 💻 Follow @madeline.m.zhang for coding memes & insights ~~~~~~~~~~~~~~~~ #learntocode #algorithms #dsa#programmingmemes #programmerhumor softwareengineer softwaredeveloper developerlife
#Lru Cache Reel by @its_.koushal - Day 18/60 - System Design Challenge Your cache is full. A new request arrives. Which data should be kicked out? 🤯 LRU? LFU? FIFO? Random? TTL? Th
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@its_.koushal
Day 18/60 – System Design Challenge Your cache is full. A new request arrives. Which data should be kicked out? 🤯 LRU? LFU? FIFO? Random? TTL? This tiny decision can decide whether your system scales smoothly or crashes under traffic. In this reel I break down the most important cache eviction strategies every backend engineer should know. 💾 Save this for system design interviews 📤 Share with a backend developer 🚀 Follow for Day 19 #systemdesign #backendengineering #caching #softwareengineering #techreels
#Lru Cache Reel by @sjain.codes - Cache replacement policies #systemdesigninterview #coding #code #google #ai
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@sjain.codes
Cache replacement policies #systemdesigninterview #coding #code #google #ai
#Lru Cache Reel by @itsgarimamalhotra - At Google, we look for Problem Solvers. 🧠🏢 Solving 500+ LeetCode problems won't save you if you can't handle the core patterns. If you're interview
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@itsgarimamalhotra
At Google, we look for Problem Solvers. 🧠🏢 Solving 500+ LeetCode problems won't save you if you can't handle the core patterns. If you're interviewing for an L4/L5 role and struggle with these specific questions, it’s usually an immediate "No-Hire." The Google "Must-Solve" List (Top 20): LRU Cache (The classic System/DSA hybrid) Word Ladder (BFS on graphs Median from Data Stream (Heaps) Course Schedule II (Topological Sort) Trapping Rain Water (Two Pointers/Monotonic Stack) Longest Directory Path (Strings/Stacks) Number of Islands (DFS/Union Find) Alien Dictionary (Graph/Topological Sort) Merge K Sorted Lists (Priority Queues) Sliding Window Maximum (Deques) Binary Tree Maximum Path Sum (Recursion) Wildcard Matching (DP) K Closest Points to Origin (QuickSelect) Find Redundant Connection (Union Find) Meeting Rooms II (Sorting/Heaps) Longest Increasing Subsequence (DP/Binary Search) Word Search II (Tries + DFS) Robot Room Cleaner (Backtracking) Smallest Range Covering K Lists (Heaps) Random Pick with Weight (Prefix Sums/Binary Search) My Advice: Don't memorize the code. Master the Pattern. If you can solve these, you can solve 90% of what we throw at you in a real interview. COMMENT DSA below and I’ll DM you the DSA pattern guide instantly. 📩 #coding #datastructures #algorithms #softwareengineer #faang . . . [FAANG Preparation, Coding Interview, Tech Interviews 2026, Placement Drive, Java, Python, c++, BigTech, best leetcode questions 2026, tech hiring, interviews]
#Lru Cache Reel by @abhishek.tech._ - 🤯 Yeh question almost har Java aur backend interview mein aata hai. Aur most developers seedha answer pe jump kar dete hain bina WHY explain kiye. Au
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@abhishek.tech._
🤯 Yeh question almost har Java aur backend interview mein aata hai. Aur most developers seedha answer pe jump kar dete hain bina WHY explain kiye. Aur yahi WHY tumhe job dilata hai. Chalte hain step by step, exactly kaise LRU Cache internally kaam karta hai aur kaunsa data structure isse O(1) mein chalata hai. 👇 INTERVIEWER PUCHTA HAI: “Design an LRU Cache. It should retrieve any item instantly, insert any item instantly, and automatically evict the least recently used item when full. What is your internal design and why?” TUMHARA ANSWER: Seedha solution pe jaane se pehle samajhte hain ki naive approach kyun fail hoti hai. Agar tum items ko ek simple list mein store karo aur har operation pe access time se sort karo toh har ek read aur write pe O(n) ka kaam hoga. Ek million operations per second pe yeh tumhara performance bilkul barbad kar dega. Isliye tumhe har single operation O(1) mein chahiye regardless of cache size. Aur yeh sirf ek very specific combination of two data structures se possible hai. Answer hai HashMap combined with a Doubly Linked List aur exactly aise kaam karta hai yeh. STRUCTURE SAMJHO PEHLE: HEAD node ek dummy sentinel hota hai list ke bilkul left mein. TAIL node ek dummy sentinel hota hai list ke bilkul right mein. Actual cache items hamesha HEAD aur TAIL ke beech mein rehte hain. Sabse important rule yeh hai: HEAD ka NEXT pointer hamesha MRU item ko point karta hai yaani jo item sabse recently use hui hai woh HEAD ke bilkul paas hoti hai. TAIL ka PREVIOUS pointer hamesha LRU item ko point karta hai yaani jo item sabse pehle evict hogi woh TAIL ke bilkul paas hoti hai. HEAD ↔ MRU ↔ .... ↔ LRU ↔ TAIL ↑ ↑ HEAD.next TAIL.prev (most recent) (evict karo) continued in comments
#Lru Cache Reel by @suryatechhub - 🔥 Cache is FULL. ⚠️ Memory limit hit. So… who gets evicted? 🤔🧠 👉 Redis decides, not your code. ⚙️ Built-in eviction strategies: 🔹 LRU - Least
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@suryatechhub
🔥 Cache is FULL. ⚠️ Memory limit hit. So… who gets evicted? 🤔🧠 👉 Redis decides, not your code. ⚙️ Built-in eviction strategies: 🔹 LRU – Least Recently Used 🔹 LFU – Least Frequently Used 🔹 FIFO – First In, First Out Wrong policy ❌ → cache misses ↑ → latency ↑ Right policy ✅ → smooth performance 🚀 📌 Save this #softwareengineer #softwaredeveloper #systemdesign #backenddeveloper #redis
#Lru Cache Reel by @java.treasure.tech - 🚀 LRU Cache (Least Recently Used) LRU Cache is a data structure that stores limited data and removes the least recently used item when the cache is f
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@java.treasure.tech
🚀 LRU Cache (Least Recently Used) LRU Cache is a data structure that stores limited data and removes the least recently used item when the cache is full. 👉 In simple terms: “The data you haven’t used for the longest time gets removed first.” 🧠 Real-Life Example Think of it like your phone apps 📱 Recently used apps → stay in memory Apps not used for long → get closed 👉 That’s exactly how LRU works 🏗️ Internal Design To achieve O(1) time complexity, LRU uses: ✔️ HashMap → Fast lookup ✔️ Doubly Linked List → Maintain usage order ⚙️ Data Structure Breakdown 🔹 HashMap Stores: key → Node Gives O(1) access Avoids traversal 🔹 Doubly Linked List Maintains order of usage Head → Most Recently Used (MRU) Tail → Least Recently Used (LRU) 👉 Why DLL? Because removal + insertion = O(1) (Singly list would fail here ❌) ⚙️ Key Operations ✅ get(key) Returns value if present 👉 Marks item as recently used ✅ put(key, value) Inserts or updates value If capacity is full: 👉 Removes least recently used (LRU) item Eviction happens in O(1) ✅ moveToHead(node) Remove node from current position Insert right after head 👉 Marks it as recently used ✅ removeTail() Remove last node (before dummy tail) Return it for map removal 👉 Always removes least used element 💡 Why This Design Works HashMap → speed DLL → order Together → O(1) + correct eviction 💡 Where It’s Used Redis / caching systems Browser cache Database query caching OS memory management 📥Save for later 📥To download free resources check link in Bio 👉Follow for more Java+System design contents
#Lru Cache Reel by @ruchit.builds.ios - Boost Your iOS App Performance with URLCache! Tired of slow API calls? It's time to level up your networking game with URLCache 1 Reduce unnecessar
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@ruchit.builds.ios
Boost Your iOS App Performance with URLCache! Tired of slow API calls? It’s time to level up your networking game with URLCache 1 Reduce unnecessary network calls 2 Improve response time 3 Enhance user experience (especially on slow networks) 4 Save battery & data usage Pro Tip: Combine caching with ETag / Last-Modified headers for smarter and more efficient data handling. If you’re preparing for iOS interviews or building scalable apps, this is a MUST-know concept Follow for more iOS + SwiftUI tips! #iosdevelopment #swiftui #swift #MVVM #techreels

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