Approaches to Array Problem Solving for Coding Interviews

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

In 2021, I failed my leetcode interviews at Amazon. Today, I conduct technical interviews at AWS. Back then, leetcode felt overwhelming. I remember staring at problems for hours wondering if I’d ever get it. I jumped between arrays, graphs and dynamic programming — hoping something would click. But it didn’t. And solving 3500+ leetcode problems 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 DSA patterns everything started to make sense. Here are the patterns that cover most interview questions: 1. Two Pointers ↳ Used for sorted arrays & strings (pair sum, removing duplicates, merging in place). 2. Fast & Slow Pointers ↳ Cycle detection, middle of linked list, detecting starting point of cycle. 3. Sliding Window ↳ Longest / shortest subarray or substring under constraints (k-distinct, frequency limits). 4. Prefix Sum ↳ Subarray sums, range queries, difference arrays. 5. Monotonic Stack / Queue ↳ Next greatest element, stock span, daily temperatures, sliding window max 6. Merge Intervals ↳ Overlapping intervals, meeting rooms, merging ranges. 7. Sorting + Sweep Line ↳ Event processing, scheduling, interval counting, greedy decisions. 8. Divide & Conquer ↳ Merge sort, quicksort, finding medians, recursive splitting. 9. Binary Search (All Variants) ↳ Sorted arrays, rotated arrays, “search on answer” for thresholds or minima. 10. Greedy Algorithms ↳ Activity selection, gas station, optimal scheduling & choice making. 11. Linked List Techniques ↳ Reversal, merging, reordering 12. Heaps & Top-K ↳ Priority queues, top-k elements, streaming median, smallest/largest k. 13. Hashmaps & Frequency Counting ↳ Anagrams, duplicates, sliding window character maps. 14. Dynamic Programming ↳ 1D/2D DP, subsequences, knapsack, DP on strings. 15. Backtracking / Recursive Search ↳ Permutations, subsets, combinations, N-Queens, search trees. 16. Graph Traversals (BFS & DFS) ↳ Connected components, islands, shortest path (unweighted), cycle detection. 17. Topological Sort (DAG Ordering) ↳ Course schedule, dependency resolution, task ordering. 18. Binary Tree Traversals (DFS/BFS) ↳ Preorder, inorder, postorder, level order traversal patterns. 19. Tree Path Problems ↳ Root-to-leaf sums, path sum, DFS with backtracking. 20. Trie (Prefix Tree) ↳ Autocomplete, prefix matching, dictionary search, word problems. Once you understand these patterns, leetcode starts to make a lot more sense. If you’re starting today: → Learn core DSA: arrays, hashmaps, stacks, queues, linked lists, graphs and trees. → Tackle one pattern at a time. Do 2–4 problems per pattern. → Create a study plan & revisit old problems as you learn new ones. → When ready, take on Blind75 questions. Save this for your next interview. Follow Abdirahman Jama for more practical software engineering tips. #softwareengineering

I’ve sat down with more than 300+ candidates for technical interviews for SDE roles in the last 6 years of my career in different leadership positions. Here are the 10 best tips I can give you from my experience on how you should approach to solve a problem during an interview: 1. Use a Heap for K Elements      - Rule: When dealing with top/maximum/minimum/closest K elements among N elements, use a Heap.      - Example Scenario: Finding the top 3 largest numbers in an array. 2. Binary Search or Two Pointers for Sorted Inputs      - Rule: If the input is a sorted array or list, use Binary Search or the Two Pointers strategy.      - Example Scenario: Finding a pair of numbers that sum up to a target in a sorted array. 3. Backtracking or BFS for Combinations      - Rule: For trying all combinations or permutations of the input, use Backtracking or Breadth-First Search (BFS).      - Example Scenario: Generating all subsets of a given set. 4. BFS or DFS for Trees and Graphs      - Rule: Most Tree or Graph questions can be solved using BFS or DFS.      - Example Scenario: Finding the shortest path in a graph. 5. Convert Recursion to Iteration with a Stack      - Rule: Every recursive solution can be rewritten as an iterative one using a Stack.      - Example Scenario: Converting recursive tree traversal to iterative using a stack. 6. Optimize Array Problems with HashMap or Sorting      - Rule: If the array solution takes O(n²) time, try using HashMap/Set for O(n) time or sorting for O(n log n) time.      - Example Scenario: Finding duplicates in an array. 7. Use Dynamic Programming for Optimization Problems      - Rule: If the problem involves maximization or minimization, use Dynamic Programming.      - Example Scenario: Solving the knapsack problem. 8. HashMap or Trie for Common Substrings      - Rule: For finding common substrings among multiple strings, use a HashMap or a Trie.      - Example Scenario: Finding the longest common prefix among strings. 9. Trie for String Search and Manipulation      - Rule: When searching or manipulating a collection of strings, a Trie is the best fit.      - Example Scenario: Implementing autocomplete functionality. 10. Fast & Slow Pointers for Linked Lists      - Rule: For Linked List problems, especially when extra space isn't allowed, use the Fast & Slow Pointer approach.      - Example Scenario: Detecting a cycle in a linked list.  ♻️ Repost if you found this helpful!

In coding interviews, most candidates focus on getting the right solution. I believe right solution is only half a solution. I will tell you why When given a problem, I don’t just start coding—I start asking. ✅ What are the constraints? ✅ Can inputs be negative? ✅ What happens when the input is empty? ✅ How does my code perform on large inputs? Once I understand the problem deeply, I don’t stop at solving it. I attack my own solution with test cases. Let’s take a simple problem: Find the Maximum Number in an Array Most candidates test it with: 🔹 [3, 1, 5, 7, 2] But I test it with: 🔹 An empty array [] (Should it return an error?) 🔹 A single-element array [42] (Does it break edge cases?) 🔹 Large input [10^6, 10^9, -10^9] (Can my code handle extremes?) 🔹 All negative numbers [-5, -2, -9] (Does it still work?) 🔹 A sorted array [1, 2, 3, 4, 5] vs. a reverse-sorted array [5, 4, 3, 2, 1] Here’s how I ensure my solution is robust: public class MaxFinder { public static int findMax(int[] arr) { if (arr == null || arr.length == 0) { throw new IllegalArgumentException("Array cannot be empty"); } int max = arr[0]; for (int num : arr) { max = Math.max(max, num); } return max; } public static void main(String[] args) { // Test cases System.out.println(findMax(new int[]{3, 1, 5, 7, 2})); // 7 System.out.println(findMax(new int[]{42})); // 42 System.out.println(findMax(new int[]{-5, -2, -9})); // -2 System.out.println(findMax(new int[]{1000000, 1000000000, -1000000000})); // 1000000000 // Edge case: empty array (should throw exception) // System.out.println(findMax(new int[]{})); // Edge case: null input (should throw exception) // System.out.println(findMax(null)); } } Why Does This Matter? Writing code is easy. Writing reliable code is what makes the difference. Interviewers don’t just want someone who solves problems. They want someone who thinks like an engineer. You need to be able to write production ready code as well. This is how you stand out. I once remember writing 15 test cases before I started solving a problem interviewer was damn impressed . 😅 Let me know what you think about this ?

7 Techniques to Solve Array Problems Struggling with array problems? Here are 7 techniques to help you crack them efficiently: Two-Pointer Technique 🏃♂️🏃♀️ Use two pointers to traverse the array from different ends. Great for problems involving pairs or finding specific elements. Sliding Window 🪟 Ideal for problems that involve contiguous subarrays. Slide a window of fixed size over the array to find the maximum, minimum, or sum. Prefix Sum & Difference Array ➕➖ Calculate running totals or differences to quickly answer range queries or find subarray sums. Hash Maps 🗺️ Use hash maps for quick lookups, like checking if a complement exists for a target sum or finding duplicates. Sorting 🧮 Sorting the array can simplify problems, especially when combined with binary search or the two-pointer technique. Dynamic Programming 📈 Break down problems into subproblems and solve them recursively. This works well for finding maximum subarray sums or counting ways to split an array. Greedy Algorithms 🤑 Make the best possible decision at each step. Useful for problems where you need to find an optimal subarray or minimize/maximize some value. Master these techniques, and you'll be solving array problems like a pro! 💡 #Coding #ArrayProblems #Techniques #ProgrammingTips #LeetCode #DataStructures #AlgorithmSkills

Cracking Leetcode problems fast during coding interviews = Knowing & recognizing the patterns. I struggled with coding interviews until I understood and implemented these 20 essential coding patterns. (Sample problems in comments)  1/ Sliding Window   - Used when handling input data in a fixed window size.   - Helps optimize problems that involve subarrays or substrings.   - Avoids brute force by moving the window dynamically.   2/ Islands (Matrix Traversal)   - Used for traversing 2D grids or matrices efficiently.   - Commonly applies DFS or BFS for searching connected components.   - Helps solve problems related to islands, clusters, or paths.   3/ Two Pointers   - Uses two pointers to traverse data, often from opposite directions.   - Helps optimize problems involving pairs, sorting, or merging.   - Reduces time complexity compared to brute force.   4/ Fast & Slow Pointers   - Also known as the Hare & Tortoise Algorithm.   - Uses two pointers moving at different speeds to detect cycles.   - Commonly used for linked lists and circular arrays.   5/ Merge Intervals   - Deals with overlapping intervals.   - Used for scheduling, reservations, and range merging.   - Sorting intervals first usually helps in solving these problems.   6/ Cyclic Sort   - Used when elements fall within a fixed range.   - Helpful for finding missing or duplicate numbers in arrays.   - Uses in-place sorting instead of extra space.   7/ In-Place Reversal of a LinkedList   - Used to reverse the links between a set of nodes in-place.   - Often needs to be done without using extra memory.   - Works well for problems involving modifying linked list structure.   8/ Breadth-First Search (BFS)   - Traverses trees or graphs level by level.   - Used for finding shortest paths or exploring neighbors first.   - Implemented using a queue.  9/ Depth-First Search (DFS)   - Traverses deep into trees or graphs before backtracking.   - Used for recursion-based traversal and path-related problems.   - Implemented using stack or recursion.   10/ Two Heaps   - Divides data into two halves: one for small elements, one for large ones.   - Uses Min-Heap and Max-Heap for quick median or priority access.   - Commonly used in streaming data or priority-based queries.   11/ Subsets   - Used for problems requiring combinations or permutations.   - Applies backtracking or BFS to generate all possible subsets.   - Useful for problems involving decision trees.   12/ Modified Binary Search   - Optimizes searching within a sorted dataset.   - Used for problems involving rotated arrays, peak elements, or range queries.   - Often leads to O(log N) complexity instead of brute force.   13/ Bitwise XOR   - Uses the XOR operator for bit manipulation problems.   - Great for finding missing or unique elements efficiently.   - Works without extra space or sorting.  Continued In Comments ↓

𝗧𝘄𝗼-𝗽𝗼𝗶𝗻𝘁𝗲𝗿𝘀 is one of the most important concepts for FAANG interviews. Follow these patterns and problems to master it. One needs practise to master it. 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝟭: 𝗜𝗻𝘄𝗮𝗿𝗱 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 (𝗠𝗲𝗲𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗶𝗱𝗱𝗹𝗲) Imagine two people walking toward each other from opposite ends of a bridge. They compare notes along the way and adjust their paths until they meet. This approach works wonders for problems where elements from opposite ends need comparison. When to use:  • Sorted arrays requiring pair comparisons (e.g., sum checks).  • Palindromic strings or symmetry-based problems.  • Finding containers with maximum capacity. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Two Sum II - Sorted Array (LeetCode 167): Find pairs in a sorted array that sum to a target. Link: https://lnkd.in/gDHwBx22 3Sum (LeetCode 15): Identify triplets that sum to zero. Link: https://lnkd.in/gYHE5pAf Container With Most Water (LeetCode 11): Maximize water trapped between vertical lines. Link: https://lnkd.in/gSvWBQmE Valid Palindrome (LeetCode 125): Check if a string reads the same backward. Link: https://lnkd.in/gXYv3W-U 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝟮: 𝗨𝗻𝗶𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 (𝗦𝗹𝗼𝘄 & 𝗙𝗮𝘀𝘁 𝗣𝗼𝗶𝗻𝘁𝗲𝗿𝘀) Picture a race between a tortoise and a hare. The tortoise (slow pointer) methodically tracks progress while the hare (fast pointer) scouts ahead. This duo excels at filtering or rearranging elements without extra space. When to use:  • Removing duplicates from sorted arrays.  • Moving zeros to the end while preserving order.  • Finding the middle of a linked list. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: Remove Duplicates from Sorted Array (LeetCode 26): Eliminate duplicates in-place. Link: https://lnkd.in/ggq9AMQv Move Zeroes (LeetCode 283): Shift zeros to the end without copying the array. Link: https://lnkd.in/g-z-RRwH Minimum Size Subarray Sum (LeetCode 209): Link: https://lnkd.in/gGPivrCF 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝟯: 𝗦𝘁𝗮𝗴𝗲𝗱 𝗧𝗿𝗮𝘃𝗲𝗿𝘀𝗮𝗹 (𝗘𝘅𝗽𝗮𝗻𝗱 & 𝗖𝗼𝗻𝗾𝘂𝗲𝗿) Think of a searchlight scanning a dark field. Once it spots something, a second light zooms in for a closer look. This method is perfect for dynamic windows or conditional searches. When to use:  • Finding the longest substring without repeating characters.  • Sliding window problems requiring adjustable bounds.  • Optimizing contiguous sequences (e.g., max profit in stock prices). Problems to Practice: Longest Substring Without Repeating Characters (LeetCode 3): Link: https://lnkd.in/gK-gVnd9 Minimum Window Substring (LeetCode 76): Link: https://lnkd.in/guMCVaV5 Sort Colors (LeetCode 75): Link: https://lnkd.in/gJHinn-F

The classic Two Sum problem is a staple in coding interviews, but problem comes in several variations — each with its own approach and tricks? Understanding these variations not only boosts your coding skills but also prepares you to tackle a wide range of interview challenges. Let’s break them down! 1️⃣ 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗧𝘄𝗼 𝗦𝘂𝗺 (𝗨𝗻𝘀𝗼𝗿𝘁𝗲𝗱 𝗔𝗿𝗿𝗮𝘆) Goal: Return indices of two numbers summing to target Approach: HashMap for O(n) time and space Why: Works on any array order 2️⃣ 𝗧𝘄𝗼 𝗦𝘂𝗺 𝗜𝗜 (𝗦𝗼𝗿𝘁𝗲𝗱 𝗔𝗿𝗿𝗮𝘆) Goal: Return indices of two numbers summing to target Approach: Two-pointer technique for O(n) time, O(1) space Why: Leverages sorted order for efficiency 3️⃣ 𝗧𝘄𝗼 𝗦𝘂𝗺 (𝗥𝗲𝘁𝘂𝗿𝗻 𝗩𝗮𝗹𝘂𝗲𝘀) Goal: Return the actual numbers, not indices Approach:Sorted? Use two-pointers Unsorted? HashMap or sort + two-pointer 4️⃣ 𝗧𝘄𝗼 𝗦𝘂𝗺 (𝗔𝗹𝗹 𝗨𝗻𝗶𝗾𝘂𝗲 𝗣𝗮𝗶𝗿𝘀 & 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴) Goal: Find all unique pairs that sum to target, even with duplicates in input Approach:   • Use a Set to track seen numbers  • Use another Set to store unique pairs (e.g., as sorted strings) to avoid duplicates  • Sort + two-pointer approach can also skip duplicates efficiently 5️⃣ 𝗢𝗻𝗹𝗶𝗻𝗲 / 𝗦𝘁𝗿𝗲𝗮𝗺 𝗩𝗮𝗿𝗶𝗮𝗻𝘁 Goal: Numbers stream in continuously, check if any pair sums to target at any time Approach: Maintain a HashSet to track complements 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Always clarify problem constraints — sorted vs unsorted, return indices vs values, uniqueness requirements — before jumping into code. If you want me to break down any variation with code examples or real interview tips, just comment below! #DataStructures #Algorithms #TwoSum #CodingInterviews #TechTips #SoftwareEngineering #InterviewPrep

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.

I analyzed the top 50 coding interview questions, and they all boil down to these 18 patterns. People ask me how to crack technical interviews without spending years on LeetCode. My secret? I don't memorize solutions. People ask how I identify the right approach for a brand new problem instantly. My secret? I look for the underlying pattern, not the specific question. But the truth is... There is no secret. Just pure Pattern Recognition. To master DSA, you have to stop treating every problem as unique. You need to map them to these 18 fundamental branches. Here is the technical breakdown to get you started: 1. Optimization & Pointers - Two Pointers: Essential for sorted arrays and linked lists. Use this to detect cycles (Floyd's algorithm), remove elements, or find target pairs in O(N) time. - Sliding Window: The standard for subarray problems. Perfect for calculating running averages, finding the longest substring under constraints, or optimization within a linear data structure. - Intervals: When dealing with time ranges or overlaps, use Merge Intervals or Catalan logic. 2. Search & Traversal - Binary Search: Beyond finding numbers. Use on rotated sorted arrays or to find "first/last occurrence" boundaries in O(log N). - Tree Traversal: Master the recursion. Know when to use Level-order (BFS) vs. Pre/In/Post-order (DFS) for tasks like serialization or finding the Lowest Common Ancestor. - Graph Traversal: Solves island counting, cycle detection, and topological sorting. 3. Complex Structures & Logic - Heaps: The most efficient way to handle "Top K" elements, scheduling tasks, or finding medians in a data stream. - Tries: The go-to design pattern for autocomplete systems, spell checkers, and prefix searches. - Backtracking: For when you need all possibilities. Used in generating permutations, N-Queens, and Sudoku solvers. 4. The Heavy Hitters - Dynamic Programming (DP): For overlapping subproblems. Includes 1D/2D arrays, Longest Common Subsequence (LCS), and the Knapsack problem. - Graph Optimization (Union Find): Critical for network connectivity. Uses Disjoint Set Union and MST algorithms like Kruskal’s or Prim’s. Want to be a software engineer? Stop memorizing. Start recognizing. Remember, seeing the pattern is 90% of the solution. The code is just syntax. Which of these patterns do you find most difficult to implement? ♻️ Repost to help a connection ace their technical interview.