Key Patterns to Master for Coding Interviews

Hard Truth: Data Structures - The Unavoidable Interview Reality Here's a pattern I've noticed recently that every software professional needs to hear: Even if you haven't used a binary tree in years, you WILL face data structure questions in your next interview. Here's why this matters: The Interview Reality Check: 1. FAANG-level companies:    - Will absolutely grill you on data structures    - Expect implementation from scratch    - Want optimal solutions 2. Startups:    - May seem more relaxed    - Still include DS questions in their process    - Use them to evaluate problem-solving 3. Even Frontend Roles:    - React state management? That's a tree    - Event handling? Welcome to queues    - Browser history? That's a stack What I've Observed: - Brilliant developers failing interviews because they're rusty on basics - Senior engineers stumbling on LinkedList questions - Tech leads getting rejected for missing optimal solutions The Smart Approach: 1. Keep a "DS Emergency Kit":    - Arrays & String manipulation    - Hash Tables implementations    - Tree traversals    - Graph basics    - Stack & Queue operations 2. Monthly Refresh Routine:    - Solve one problem per structure    - Review time complexities    - Practice explaining your approach Common Mistakes: - Thinking "I don't use this at work, so I won't study it" - Starting interview prep too late - Focusing only on coding, ignoring theory Quick Tips: 1. LeetCode Medium is your friend 2. Always write clean code in interviews 3. Think aloud during problem-solving 4. Review basic implementations monthly Core Data Structures You MUST Know: 1. Arrays    - What: Continuous memory blocks    - Why: Foundation of most data operations    - Real use: Instagram's photo feed, Spotify's playlist management 2. Linked Lists    - What: Connected nodes with next/prev references    - Why: Dynamic memory allocation    - Real use: Undo/Redo functionality in text editors 3. Hash Tables    - What: Key-value pair storage    - Why: Lightning-fast O(1) lookups    - Real use: Database indexing, caching systems 4. Stacks (LIFO)    - What: Last-In-First-Out structure    - Why: Track execution context    - Real use: Browser history, Function call management 5. Queues (FIFO)    - What: First-In-First-Out structure    - Why: Order preservation    - Real use: Print spoolers, Message queues in distributed systems 6. Trees    - What: Hierarchical data structure    - Why: Organized data relationships    - Real use: File systems, DOM in web browsers 7. Graphs    - What: Nodes connected by edges    - Why: Complex relationship mapping    - Real use: Social networks, Google Maps, Netflix recommendations 1. Practice implementing from scratch 2. Study time complexity for each operation 3. Learn when to use which structure Action Items: 1. Pick one structure weekly 2. Implement it in your preferred language 3. Solve 2-3 related problems 4. Document real-world applications

I failed 30+ coding interviews until I understood and applied these concepts.  I’ve applied and interviewed at every MAANG company as well as countless startups across Malaysia, India and the USA. I’ve had many rejections but each has taught me something great about how tech hiring works.  Here’s what I wish someone had told me earlier: Let me break it down for you 👇 1️⃣ Arrays & Patterns → Learn how to move pointers with intention:   Two Pointers, Sliding Window, Prefix Sums.  → Add Binary Search + Sorting, and you can solve 30% of Leetcode. 2️⃣ Recursion & Decision Trees → If you're scared of recursion, you haven’t seen it as a decision tree. This one shift helped me unlock: ✅ Backtracking   ✅ Binary Tree Traversals   ✅ DP States   ✅ Even Subset & Combination problems  See the tree. Build the logic. Code will follow. 3️⃣ Graphs → 90% of graph questions are DFS or BFS.   → The real twist? Whether it’s on a matrix or an adjacency list.  Master:   ✅ Cycle Detection   ✅ Shortest Path   ✅ Connected Components Graphs only feel scary when you don’t code them enough. 4️⃣ HashMaps The hidden hero of 100+ problems. → Use for frequencies, fast lookups, and avoiding O(N²) traps.   → Ask yourself in slow problems: “Can I precompute with a HashMap?” Interviewers LOVE clean hashmap tricks.   Show them, don’t just tell them. 5️⃣ Heaps → Top K problems? Min-Heap.   → Frequent Elements? Max-Heap.   → Sliding Window Median? You’ll need two heaps. 6️⃣ Dynamic Programming Yes, it's hard.   But no, you don’t need to do 100+ DP problems. Start with: ✅ LCS   ✅ 0/1 Knapsack   ✅ LIS   ✅ Partition Equal Subset  Master 1D → 2D transitions and memo → tabulation.   That’s 90% of the game. You don’t need quantity, you need clarity. → 80 solid problems solved deeply > 300 solved mindlessly   → Debug out loud, talk through your approach, focus on patterns And above all? Don’t let rejection stop you. Let it refine you. You've got this    If you’re in the grind right now, I see you. You're closer than you think.

I spent 2+ years grinding Leetcode and learning DSA & problem-solving as a CSE student at BITS Pilani. If I had to start again from 0, this is exactly how I’d do it to get hired as a Software Engineer in 2025:  1️⃣ 𝗺𝗮𝘀𝘁𝗲𝗿 𝗰𝗼𝗿𝗲 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 ○ Before touching random Leetcode problems, I’d master core algorithms like: Binary Search, DFS, BFS, Sliding Window, Two Pointers, Recursion, Backtracking, and Dynamic Programming. ○ The goal isn’t just to “understand” these but to be able to implement them from scratch without hesitation. ○ I’d write each algorithm at least 5-10 times from memory until it becomes second nature.  2️⃣ 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 ○The biggest mistake people make is solving hundreds of random problems without recognizing patterns. ○ 90% of interview problems fall into ~20 coding patterns. ○ Instead of solving 1000+ questions, I’d focus on understanding:    ►How problems within a pattern are similar.    ►What small variations make them different.    ►Which techniques help optimize them.  3️⃣ 𝗿𝗲𝗽𝗲𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗷𝘂𝘀𝘁 𝗺𝗼𝘃𝗶𝗻𝗴 𝗼𝗻 ○ Just because I solved a problem once doesn’t mean I’ll remember it. ○ I’d follow this reinforcement schedule for problems I struggled with: Solve it today, solve it again in 1 week and finally solve it again in 1 month. ○This is how I’d make sure I actually retain what I learn.  4️⃣ 𝘀𝘁𝘂𝗱𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆 ○ When I can’t solve a problem, I’d first struggle with it for at least 30 minutes before looking at the solution. ○ Then, I’d:   ► Write out the solution from memory after understanding. ► Explain why it works in simple words ► (if I can’t explain it, I don’t truly understand it).   ► Look at alternative solutions to compare trade-offs.  5️⃣ 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗹𝗶𝘀𝘁 (𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗮𝗻𝗱𝗼𝗺 𝗴𝗿𝗶𝗻𝗱𝗶𝗻𝗴) ○ Instead of jumping between random problems, I’d follow a structured roadmap like:  ► First 50-60 easy problems → Get comfortable with implementation.   ► Next 80-100 medium problems → Focus on pattern recognition & optimization.   ► Last 30-40 hard problems → Only after mastering the fundamentals.  ○ 150-200 well-selected problems are enough to crack most interviews.  6️⃣ 𝘀𝘁𝗮𝗿𝘁 𝗺𝗼𝗰𝗸 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗲𝗮𝗿𝗹𝘆 (𝗲𝘃𝗲𝗻 𝘄𝗵𝗲𝗻 𝗜 𝗳𝗲𝗹𝘁 𝘂𝗻𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱)   ○ Simulating real interviews early helps with time pressure & communication skills.  ○ I’d start setting strict 30-45 min timers on problems to simulate interview conditions.  ○ I’d practice explaining my thought process out loud, since half of the interview is about communication.  (Bonus tip in the comments) ↓

🚀 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

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

After giving & conducting 100+ coding interviews over the past 12+ years at startups and MAANG+ companies, I’ve realized one thing: —Interviews aren’t about who writes the most code. —they’re about who thinks in the most structured way. Here are 30 key insights I’ve learned from sitting on both sides of the table - as an interviewee striving to prove my skills and as an interviewer evaluating candidates:   - Communicate trade-offs clearly - Test your code with multiple cases - Start with a brute force, then improve - Clarify edge cases & constraints early - Know when to trade-off time vs. space - Explain time & space complexity upfront - Know when to use BFS vs. DFS in graphs - Optimize only after correctness is ensured - Don’t overcomplicate: simple solutions win - Think out loud & show your thought process - Handle errors & unexpected inputs gracefully - Use stack for problems with nested structures - Understand system design even at junior levels - Recognize patterns (many problems are variations) - Cache results for repeated calculations (Memoization) - Understand when to use heaps & priority queues - Confidence matters(believe in your approach) - Master sliding window for subarray questions - Use binary search for optimization problems - Use modular functions for better readability - Know when recursion vs. iteration is better - Use meaningful variable & function names - Write clean, readable, and modular code - Divide & conquer for large problem sets - Refactor before finalizing your solution Understand the problem before coding - Use two pointers for array problems - Use hash maps for quick lookups - Know how to debug efficiently At the end of the day, coding interviews aren’t about memorization, they’re about structured thinking.  Which lesson do you wish you knew earlier?

𝐓𝐫𝐲 𝐭𝐡𝐢𝐬 𝐡𝐚𝐜𝐤 𝐭𝐨 𝐚𝐜𝐞 𝐲𝐨𝐮𝐫 𝐜𝐨𝐝𝐢𝐧𝐠 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐩𝐫𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧. LeetCode has around 3K problems. With this huge set of problems, the biggest challenge is where to start or what to focus on. I have helped many engineers to prepare for their interviews. After talking to many developers who recently went through interviews and keeping the LeetCode problems distribution in mind, I came up with the top 7 coding patterns with the highest ROI. To prepare smartly, you should not ignore these patterns. Here is the list: 💠Two Pointers 💠Sliding Window 💠Depth-First Search & Breadth-First Search 💠Binary Search 💠Interval Merge 💠Recursion/Backtracking Each pattern offers a strategic approach for understanding and solving a wide array of coding problems. These patterns are the keystones that will enable you to recognize problem types and apply familiar solutions, saving you precious time and mental energy during high-stakes interviews. Ref: 𝐆𝐫𝐨𝐤𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐂𝐨𝐝𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 - https://lnkd.in/g6ApdjVW