Key DSA Patterns for Google and Twitter Interviews

Most people don’t fail in DSA because it’s hard. They fail because they try to learn everything instead of learning the right patterns. If you’re a fresher preparing for coding interviews, stop collecting questions. Start mastering patterns. Here’s the exact roadmap I recommend 👇 1️⃣ Arrays Core patterns you must know: • Two Pointers • Sliding Window (fixed and variable) • Prefix Sum • Kadane’s Algorithm • Hashing / Frequency Map • Sorting + Greedy • Cyclic Sort • Binary Search 2️⃣ Linked Lists Core patterns: • Fast & Slow Pointer • Dummy Node • Reversal (entire list / k-group) • Merge Lists • Pointer Rewiring 3️⃣ Stack & Queue Core patterns: • Monotonic Stack • Monotonic Queue • Stack for Previous / Next Greater • Sliding Window + Deque 4️⃣ Trees & Graphs Core patterns: • DFS (pre / in / post order) • BFS (level order) • Recursion • Backtracking on Trees • Dijkstra • Topological Sort • Union Find 5️⃣ Advanced Patterns • Binary Search on Answer • Greedy • Dynamic Programming ◦ 0/1 Knapsack ◦ Unbounded Knapsack ◦ DP on Strings • Heap (Top K, Merge K) • Bit Manipulation You don’t need 1000 problems. You need clarity on these patterns. Once you understand the pattern, 10 different questions start looking the same. That’s when preparation becomes smart. If you’re preparing for placements or switching jobs, save this post and follow for more such content ❤️

This is the exact DSA notes strategy I use. I don’t make notes topic-wise. I make them pattern-wise. Sliding window. Two pointer. 2D DP. Because interviews don’t test chapters. They test patterns. For every problem, my notes have only five things. 1. Pattern of the problem So I know where this problem fits. 2. Key point of the optimal approach Just the idea that makes the solution work. 3. Time and space complexity movement Very useful in exams and interviews. For example, in top-K problems: O(n²) → O(nk) → O(n log k) Same logic for space complexity. 4. No copy-pasting full solutions I write only the core idea. If I can’t rebuild the code from that, I don’t understand it yet. 5. Short hints for future revision One-liners that help me recall the approach quickly. That’s it. Short notes. Clear thinking. Easier revision. If you want a structured pattern-wise DSA sheet, this one explains patterns really well: (https://lnkd.in/gYV5weys) #DSA #InterviewPrep #ProblemSolving #LeetCode #GFG #EngineeringLife #TechLearning #risingbrain

🚀 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗜 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Over time, I’ve noticed that most problems follow certain patterns. Identifying the right pattern early makes solving problems much easier. Here are the ones I focus on the most: ✅ 𝗜𝗳 𝘁𝗵𝗲 𝗶𝗻𝗽𝘂𝘁 𝗮𝗿𝗿𝗮𝘆 𝗶𝘀 𝘀𝗼𝗿𝘁𝗲𝗱, 𝘁𝗵𝗲𝗻: Binary search (O(log N) for efficient searching) Two pointers (Optimizing sum/pair problems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗮𝗹𝗹 𝗽𝗲𝗿𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀/𝘀𝘂𝗯𝘀𝗲𝘁𝘀, 𝘁𝗵𝗲𝗻: Backtracking (Explore all possible solutions) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝘁𝗿𝗲𝗲, 𝘁𝗵𝗲𝗻: DFS (Depth-first for deep traversal) BFS (Level-order traversal) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗴𝗿𝗮𝗽𝗵, 𝘁𝗵𝗲𝗻: DFS (Finding connected components, cycles) BFS (Shortest path, traversals) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗹𝗶𝗻𝗸𝗲𝗱 𝗹𝗶𝘀𝘁, 𝘁𝗵𝗲𝗻: Two pointers (Fast & slow for cycle detection, middle node, etc.) ✅ 𝗜𝗳 𝗿𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻 𝗶𝘀 𝗯𝗮𝗻𝗻𝗲𝗱, 𝘁𝗵𝗲𝗻: Stack (Simulate recursion iteratively) ✅ 𝗜𝗳 𝗺𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗲 𝗶𝗻-𝗽𝗹𝗮𝗰𝗲, 𝘁𝗵𝗲𝗻: Swap corresponding values (For sorting or reordering) Store multiple values in the same pointer (Memory optimization) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗺𝗮𝘅𝗶𝗺𝘂𝗺/𝗺𝗶𝗻𝗶𝗺𝘂𝗺 𝘀𝘂𝗯𝗮𝗿𝗿𝗮𝘆/𝘀𝘂𝗯𝘀𝗲𝘁/𝗼𝗽𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝗻: Dynamic Programming (DP) (Break into overlapping subproblems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝘁𝗼𝗽/𝗹𝗲𝗮𝘀𝘁 𝗞 𝗶𝘁𝗲𝗺𝘀, 𝘁𝗵𝗲𝗻: Heap (Efficient priority-based extraction) QuickSelect (Optimized selection of Kth smallest/largest) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝗼𝗻 𝘀𝘁𝗿𝗶𝗻𝗴𝘀, 𝘁𝗵𝗲𝗻: Map (HashMap) (Quick lookup & frequency counting) Trie (Efficient prefix-based searching) ✅ 𝗘𝗹𝘀𝗲: Map/Set (O(1) lookup, O(N) space) Sort input (O(N log N) time, O(1) space) These patterns help break down complex problems and quickly guide me toward an optimal solution. 🚀 💬 Which pattern do you find most useful in interviews? Let’s discuss! #LeetCode #DSA #JobSearch #CodingInterview #FAANG #MAANG #Amazon #Meta #Google #Microsoft #Netflix #Apple #Blind75 #Grind75 #TechInterview #SoftwareEngineering #Algorithms #DataStructures #InterviewPreparation #ProblemSolving #CrackingTheCodingInterview #Careers #SystemDesign #CompetitiveProgramming #FullStackDeveloper #DevCommunity #CodeNewbie #TechCareers #JobSearch #Python #Java #CodingLife #TechIndustry

Nobody tells you this about DSA interviews: You don’t need to solve 1000 problems. You need to recognise 15 patterns. Most candidates grind blindly. Top candidates recognise structures. Here are the patterns that show up in 80% of interviews: 1️⃣ Sliding Window → Subarray / substring problems 2️⃣ Two Pointers → Sorted arrays, pair finding 3️⃣ Fast & Slow Pointers → Cycle detection in linked lists 4️⃣ Merge Intervals → Overlapping ranges 5️⃣ Binary Search (Modified) → Not just sorted arrays 6️⃣ BFS / Level Order → Shortest path, level-by-level traversal 7️⃣ DFS + Backtracking → Permutations, combinations, grid paths 8️⃣ Dynamic Programming → Overlapping subproblems, optimal decisions 9️⃣ Monotonic Stack → Next greater / smaller element 🔟 Heap / Top-K → K largest elements, streaming data Once you see the pattern - you stop memorising solutions. You start building them. That’s the difference between: ❌ Random LeetCode grinding ✅ Structured interview preparation This is exactly how we train at our cohort. Not hustle. Not guesswork. Pattern recognition + system thinking. If you’re preparing for product companies -this shift changes everything. Which of these patterns do you struggle with the most? Comment below 👇

Top 5 Must-Know DSA Patterns👇🏻👇🏻 DSA problems often follow recurring patterns. Mastering these patterns can make problem-solving more efficient and help you ace coding interviews. Here’s a quick breakdown: 1. Sliding Window • Use Case: Solves problems involving contiguous subarrays or substrings. • Key Idea: Slide a window over the data to dynamically track subsets. • Examples: • Maximum sum of subarray of size k. • Longest substring without repeating characters. 2. Two Pointers • Use Case: Optimizes array problems involving pairs or triplets of elements. • Key Idea: Use two pointers to traverse from opposite ends or incrementally. • Examples: • Pair with target sum in a sorted array. • Trapping rainwater problem. 3. Binary Search • Use Case: Efficiently solves problems with sorted data or requiring optimization. • Key Idea: Repeatedly halve the search space to narrow down the solution. • Examples: • Find an element in a sorted array. • Search in a rotated sorted array. 4. Dynamic Programming (DP) • Use Case: Handles problems with overlapping subproblems and optimal substructure. • Key Idea: Build solutions iteratively using a table to store intermediate results. • Examples: • 0/1 Knapsack problem. • Longest common subsequence. 5. Backtracking • Use Case: Solves problems involving all possible combinations, subsets, or arrangements. • Key Idea: Incrementally build solutions and backtrack when a condition is not met. • Examples: • N-Queens problem. • Sudoku solver. Why These Patterns? By focusing on patterns, you can identify the right approach quickly, saving time and improving efficiency in problem-solving.

Three years ago, I was deep in interview prep for roles at Amazon, Google and Meta. And Leetcode almost broke me. I remember staring at problems for hours, wondering if I'd ever get it. I tried solving everything randomly, Arrays, Graphs, DP — hoping something would click. But solving 3500+ questions felt impossible. Then I realised something that changed everything: It’s not about solving thousands of problems. It’s about learning the patterns behind them. Once I focused on the core data structure and algorithm (DSA) patterns, everything started to make sense. Here are 25 DSA coding patterns that cover most interview questions: 1. Two Pointers 2. Fast & Slow Pointers 3. Sliding Window 4. Prefix Sum 5. Monotonic Stack / Queue 6. Merge Intervals 7. Sorting-Based Patterns 8. Divide & Conquer 9. Binary Search (and Variants) 10. Overlapping Intervals 11. Greedy Algorithms 12. Linked List Techniques (Dummy Node, In-place Reversal) 13. Top-K Elements (Heap / QuickSelect) 14. Kth Largest/Smallest Elements 15. Hashmaps & Frequency Counting 16. Dynamic Programming (1D, 2D, Knapsack, Range DP) 17. Backtracking & Recursive Search 18. Graph Traversals (BFS, DFS) 19. Topological Sort 20. Binary Tree Traversals (Preorder, Inorder, Postorder, Level Order) 21. Path Sum & Root-to-Leaf Techniques 22. Matrix Traversal 23. Expression Evaluation (Stacks & Queues) 24. Design Problems (LRU Cache, Twitter, Rate Limiter, etc.) 25. Trie / Prefix Tree Once you understand these patterns, you’ll start recognising them everywhere — from Leetcode to real-world systems. You don’t need to solve thousands of questions to land great offers. You just need to master these patterns and practice them deliberately. If you’re starting today: → Learn the core data structures first: Arrays, Linked Lists, Stacks, Queues, Hashmaps, Heaps, Trees, Graphs, Tries, and Matrices. → Check out Taro (YC S22) 75 questions by Alex and Rahul. Curated beautifully around these patterns. → Give NeetCode a go — Navdeep did a cracking job explaining the patterns above. This is how you go from frustration to confidence. And from endless grinding to real results. #softwareengineering #leetcode

I solved 400+ LeetCode problems consistently. Still got stuck in interviews midway. Why? Because I didn’t study patterns, I studied just problems. If you're grinding for tech interviews at FAANG or top startups, don’t just brute-force your way through LeetCode. I wasted months before realizing this truth: every top company tests a repeatable set of patterns. Here’s what helped me actually get better and get tech interviews: ✅ Salesforce = hashmap, heap, string manipulation, and recursion. Expect edge cases and real-world data constraints. I used this company-tagged problem list to spot repeated trends.  ✅ Google = loves recursion and trees. Think: backtracking, permutations, and k-ary structures. This LeetCode list helped me master their common types. ✅ Amazon = sliding windows, greedy, and 2 pointers. They test for speed and optimization. Use this Grokking course to drill patterns like sliding window and merge intervals until they’re second nature. ✅ Meta = graphs and BFS/DFS variants. Trust me, you can’t fake graph intuition. NeetCode’s graph sheet was a game-changer for me. ✅ Uber = dynamic programming, interval scheduling, and graphs. They care about your ability to model real-world systems. Practice problems involving route planning, time optimization, and caching logic. The Lesson I learned here? The ultimate shortcut is knowing which coding patterns to master for which company. Check out my DSA Handbook here: https://lnkd.in/eyu9rBn3 Save this post so you’re not solving random LeetCode problems before your next big interview. Tag a friend who’s deep into the coding interview prep mode. Follow Tharun Kumar Reddy Polu for targeted, real-world strategies to land top tech roles. #LeetCode #FAANG #DSA #TechInterviewTips #SoftwareEngineering #Mentorship